query: As an environmental researcher, I aim to create a mobile application that uses generative adversarial networks (GANs) to produce personalized, vivid images illustrating the potential impacts of climate change on specific locations. My plan is to train the GAN on street-view images of horses before and after extreme weather events. However, since such a dataset does not exist, I require a model capable of learning the 3D structure of an object from images taken from various angles and subsequently rendering an image of the structure from a specified direction. Furthermore, I intend for the application to incorporate climate models to evaluate the probability and type of long-term climate-related events. As I am developing a mobile application, I also need techniques to reduce the latency between the mobile device and the backend server, as well as strategies to manage multiple simultaneous calls to the backend server. Lastly, I am interested in video compression algorithms that can enhance the upload speed and alleviate the storage pressure on the server.

negative:
In this paper we present, to the best of our knowledge, the first method to
learn a generative model of 3D shapes from natural images in a fully
unsupervised way. For example, we do not use any ground truth 3D or 2D
annotations, stereo video, and ego-motion during the training. Our approach
follows the general strategy of Generative Adversarial Networks, where an image
generator network learns to create image samples that are realistic enough to
fool a discriminator network into believing that they are natural images. In
contrast, in our approach the image generation is split into 2 stages. In the
first stage a generator network outputs 3D objects. In the second, a
differentiable renderer produces an image of the 3D objects from random
viewpoints. The key observation is that a realistic 3D object should yield a
realistic rendering from any plausible viewpoint. Thus, by randomizing the
choice of the viewpoint our proposed training forces the generator network to
learn an interpretable 3D representation disentangled from the viewpoint. In
this work, a 3D representation consists of a triangle mesh and a texture map
that is used to color the triangle surface by using the UV-mapping technique.
We provide analysis of our learning approach, expose its ambiguities and show
how to overcome them. Experimentally, we demonstrate that our method can learn
realistic 3D shapes of faces by using only the natural images of the FFHQ
dataset.

negative:
Current Generative Adversarial Networks (GANs) produce photorealistic
renderings of portrait images. Embedding real images into the latent space of
such models enables high-level image editing. While recent methods provide
considerable semantic control over the (re-)generated images, they can only
generate a limited set of viewpoints and cannot explicitly control the camera.
Such 3D camera control is required for 3D virtual and mixed reality
applications. In our solution, we use a few images of a face to perform 3D
reconstruction, and we introduce the notion of the GAN camera manifold, the key
element allowing us to precisely define the range of images that the GAN can
reproduce in a stable manner. We train a small face-specific neural implicit
representation network to map a captured face to this manifold and complement
it with a warping scheme to obtain free-viewpoint novel-view synthesis. We show
how our approach - due to its precise camera control - enables the integration
of a pre-trained StyleGAN into standard 3D rendering pipelines, allowing e.g.,
stereo rendering or consistent insertion of faces in synthetic 3D environments.
Our solution proposes the first truly free-viewpoint rendering of realistic
faces at interactive rates, using only a small number of casual photos as
input, while simultaneously allowing semantic editing capabilities, such as
facial expression or lighting changes.

negative:
Climate change is a major threat to humanity, and the actions required to
prevent its catastrophic consequences include changes in both policy-making and
individual behaviour. However, taking action requires understanding the effects
of climate change, even though they may seem abstract and distant. Projecting
the potential consequences of extreme climate events such as flooding in
familiar places can help make the abstract impacts of climate change more
concrete and encourage action. As part of a larger initiative to build a
website that projects extreme climate events onto user-chosen photos, we
present our solution to simulate photo-realistic floods on authentic images. To
address this complex task in the absence of suitable training data, we propose
ClimateGAN, a model that leverages both simulated and real data for
unsupervised domain adaptation and conditional image generation. In this paper,
we describe the details of our framework, thoroughly evaluate components of our
architecture and demonstrate that our model is capable of robustly generating
photo-realistic flooding.

negative:
Detecting extreme events in large datasets is a major challenge in climate
science research. Current algorithms for extreme event detection are build upon
human expertise in defining events based on subjective thresholds of relevant
physical variables. Often, multiple competing methods produce vastly different
results on the same dataset. Accurate characterization of extreme events in
climate simulations and observational data archives is critical for
understanding the trends and potential impacts of such events in a climate
change content. This study presents the first application of Deep Learning
techniques as alternative methodology for climate extreme events detection.
Deep neural networks are able to learn high-level representations of a broad
class of patterns from labeled data. In this work, we developed deep
Convolutional Neural Network (CNN) classification system and demonstrated the
usefulness of Deep Learning technique for tackling climate pattern detection
problems. Coupled with Bayesian based hyper-parameter optimization scheme, our
deep CNN system achieves 89\%-99\% of accuracy in detecting extreme events
(Tropical Cyclones, Atmospheric Rivers and Weather Fronts

negative:
As climate change increases the intensity of natural disasters, society needs
better tools for adaptation. Floods, for example, are the most frequent natural
disaster, but during hurricanes the area is largely covered by clouds and
emergency managers must rely on nonintuitive flood visualizations for mission
planning. To assist these emergency managers, we have created a deep learning
pipeline that generates visual satellite images of current and future coastal
flooding. We advanced a state-of-the-art GAN called pix2pixHD, such that it
produces imagery that is physically-consistent with the output of an
expert-validated storm surge model (NOAA SLOSH). By evaluating the imagery
relative to physics-based flood maps, we find that our proposed framework
outperforms baseline models in both physical-consistency and photorealism.
While this work focused on the visualization of coastal floods, we envision the
creation of a global visualization of how climate change will shape our earth.

negative:
Using generative models for Inverse Graphics is an active area of research.
However, most works focus on developing models for supervised and
semi-supervised methods. In this paper, we study the problem of unsupervised
learning of 3D geometry from single images. Our approach is to use a generative
model that produces 2-D images as projections of a latent 3D voxel grid, which
we train either as a variational auto-encoder or using adversarial methods. Our
contributions are as follows: First, we show how to recover 3D shape and pose
from general datasets such as MNIST, and MNIST Fashion in good quality. Second,
we compare the shapes learned using adversarial and variational methods.
Adversarial approach gives denser 3D shapes. Third, we explore the idea of
modelling the pose of an object as uniform distribution to recover 3D shape
from a single image. Our experiment with the CelebA dataset
\cite{liu2015faceattributes} proves that we can recover complete 3D shape from
a single image when the object is symmetric along one, or more axis whilst
results obtained using ModelNet40 \cite{wu20153d} show the potential
side-effects, in which the model learns 3D shapes such that it can render the
same image from any viewpoint. Forth, we present a general end-to-end approach
to learning 3D shapes from single images in a completely unsupervised fashion
by modelling the factors of variation such as azimuth as independent latent
variables. Our method makes no assumptions about the dataset, and can work with
synthetic as well as real images (i.e. unsupervised in true sense). We present
our results, by training the model using the $\mu$-VAE objective
\cite{ucar2019bridging} and a dataset combining all images from MNIST, MNIST
Fashion, CelebA and six categories of ModelNet40. The model is able to learn 3D
shapes and the pose in qood quality and leverages information learned across
all datasets.

negative:
We propose a learned image-guided rendering technique that combines the
benefits of image-based rendering and GAN-based image synthesis. The goal of
our method is to generate photo-realistic re-renderings of reconstructed
objects for virtual and augmented reality applications (e.g., virtual
showrooms, virtual tours \& sightseeing, the digital inspection of historical
artifacts). A core component of our work is the handling of view-dependent
effects. Specifically, we directly train an object-specific deep neural network
to synthesize the view-dependent appearance of an object. As input data we are
using an RGB video of the object. This video is used to reconstruct a proxy
geometry of the object via multi-view stereo. Based on this 3D proxy, the
appearance of a captured view can be warped into a new target view as in
classical image-based rendering. This warping assumes diffuse surfaces, in case
of view-dependent effects, such as specular highlights, it leads to artifacts.
To this end, we propose EffectsNet, a deep neural network that predicts
view-dependent effects. Based on these estimations, we are able to convert
observed images to diffuse images. These diffuse images can be projected into
other views. In the target view, our pipeline reinserts the new view-dependent
effects. To composite multiple reprojected images to a final output, we learn a
composition network that outputs photo-realistic results. Using this
image-guided approach, the network does not have to allocate capacity on
``remembering'' object appearance, instead it learns how to combine the
appearance of captured images. We demonstrate the effectiveness of our approach
both qualitatively and quantitatively on synthetic as well as on real data.

negative:
We present a method to learn the 3D surface of objects directly from a
collection of images. Previous work achieved this capability by exploiting
additional manual annotation, such as object pose, 3D surface templates,
temporal continuity of videos, manually selected landmarks, and
foreground/background masks. In contrast, our method does not make use of any
such annotation. Rather, it builds a generative model, a convolutional neural
network, which, given a noise vector sample, outputs the 3D surface and texture
of an object and a background image. These 3 components combined with an
additional random viewpoint vector are then fed to a differential renderer to
produce a view of the sampled object and background. Our general principle is
that if the output of the renderer, the generated image, is realistic, then its
input, the generated 3D and texture, should also be realistic. To achieve
realism, the generative model is trained adversarially against a discriminator
that tries to distinguish between the output of the renderer and real images
from the given data set. Moreover, our generative model can be paired with an
encoder and trained as an autoencoder, to automatically extract the 3D shape,
texture and pose of the object in an image. Our trained generative model and
encoder show promising results both on real and synthetic data, which
demonstrate for the first time that fully unsupervised 3D learning from image
collections is possible.

negative:
Recent improvements to Generative Adversarial Networks (GANs) have made it
possible to generate realistic images in high resolution based on natural
language descriptions such as image captions. Furthermore, conditional GANs
allow us to control the image generation process through labels or even natural
language descriptions. However, fine-grained control of the image layout, i.e.
where in the image specific objects should be located, is still difficult to
achieve. This is especially true for images that should contain multiple
distinct objects at different spatial locations. We introduce a new approach
which allows us to control the location of arbitrarily many objects within an
image by adding an object pathway to both the generator and the discriminator.
Our approach does not need a detailed semantic layout but only bounding boxes
and the respective labels of the desired objects are needed. The object pathway
focuses solely on the individual objects and is iteratively applied at the
locations specified by the bounding boxes. The global pathway focuses on the
image background and the general image layout. We perform experiments on the
Multi-MNIST, CLEVR, and the more complex MS-COCO data set. Our experiments show
that through the use of the object pathway we can control object locations
within images and can model complex scenes with multiple objects at various
locations. We further show that the object pathway focuses on the individual
objects and learns features relevant for these, while the global pathway
focuses on global image characteristics and the image background.

negative:
In recent years, research on image generation methods has been developing
fast. The auto-encoding variational Bayes method (VAEs) was proposed in 2013,
which uses variational inference to learn a latent space from the image
database and then generates images using the decoder. The generative
adversarial networks (GANs) came out as a promising framework, which uses
adversarial training to improve the generative ability of the generator.
However, the generated pictures by GANs are generally blurry. The deep
convolutional generative adversarial networks (DCGANs) were then proposed to
leverage the quality of generated images. Since the input noise vectors are
randomly sampled from a Gaussian distribution, the generator has to map from a
whole normal distribution to the images. This makes DCGANs unable to reflect
the inherent structure of the training data. In this paper, we propose a novel
deep model, called generative adversarial networks with decoder-encoder output
noise (DE-GANs), which takes advantage of both the adversarial training and the
variational Bayesain inference to improve the performance of image generation.
DE-GANs use a pre-trained decoder-encoder architecture to map the random
Gaussian noise vectors to informative ones and pass them to the generator of
the adversarial networks. Since the decoder-encoder architecture is trained by
the same images as the generators, the output vectors could carry the intrinsic
distribution information of the original images. Moreover, the loss function of
DE-GANs is different from GANs and DCGANs. A hidden-space loss function is
added to the adversarial loss function to enhance the robustness of the model.
Extensive empirical results show that DE-GANs can accelerate the convergence of
the adversarial training process and improve the quality of the generated
images.

negative:
We give an overview of the different rendering methods and we demonstrate
that the use of a Generative Adversarial Networks (GAN) for Global Illumination
(GI) gives a superior quality rendered image to that of a rasterisations image.
We utilise the Pix2Pix architecture and specify the hyper-parameters and
methodology used to mimic ray-traced images from a set of input features. We
also demonstrate that the GANs quality is comparable to the quality of the
ray-traced images, but is able to produce the image, at a fraction of the time.

negative:
Generative Adversarial Networks (GAN) have many potential medical imaging
applications, including data augmentation, domain adaptation, and model
explanation. Due to the limited memory of Graphical Processing Units (GPUs),
most current 3D GAN models are trained on low-resolution medical images, these
models either cannot scale to high-resolution or are prone to patchy artifacts.
In this work, we propose a novel end-to-end GAN architecture that can generate
high-resolution 3D images. We achieve this goal by using different
configurations between training and inference. During training, we adopt a
hierarchical structure that simultaneously generates a low-resolution version
of the image and a randomly selected sub-volume of the high-resolution image.
The hierarchical design has two advantages: First, the memory demand for
training on high-resolution images is amortized among sub-volumes. Furthermore,
anchoring the high-resolution sub-volumes to a single low-resolution image
ensures anatomical consistency between sub-volumes. During inference, our model
can directly generate full high-resolution images. We also incorporate an
encoder with a similar hierarchical structure into the model to extract
features from the images. Experiments on 3D thorax CT and brain MRI demonstrate
that our approach outperforms state of the art in image generation. We also
demonstrate clinical applications of the proposed model in data augmentation
and clinical-relevant feature extraction.

negative:
Then detection and identification of extreme weather events in large-scale
climate simulations is an important problem for risk management, informing
governmental policy decisions and advancing our basic understanding of the
climate system. Recent work has shown that fully supervised convolutional
neural networks (CNNs) can yield acceptable accuracy for classifying well-known
types of extreme weather events when large amounts of labeled data are
available. However, many different types of spatially localized climate
patterns are of interest including hurricanes, extra-tropical cyclones, weather
fronts, and blocking events among others. Existing labeled data for these
patterns can be incomplete in various ways, such as covering only certain years
or geographic areas and having false negatives. This type of climate data
therefore poses a number of interesting machine learning challenges. We present
a multichannel spatiotemporal CNN architecture for semi-supervised bounding box
prediction and exploratory data analysis. We demonstrate that our approach is
able to leverage temporal information and unlabeled data to improve the
localization of extreme weather events. Further, we explore the representations
learned by our model in order to better understand this important data. We
present a dataset, ExtremeWeather, to encourage machine learning research in
this area and to help facilitate further work in understanding and mitigating
the effects of climate change. The dataset is available at
extremeweatherdataset.github.io and the code is available at
https://github.com/eracah/hur-detect.

negative:
Landscapes are meaningful ecological units that strongly depend on the
environmental conditions. Such dependencies between landscapes and the
environment have been noted since the beginning of Earth sciences and cast into
conceptual models describing the interdependencies of climate, geology,
vegetation and geomorphology. Here, we ask whether landscapes, as seen from
space, can be statistically predicted from pertinent environmental conditions.
To this end we adapted a deep learning generative model in order to establish
the relationship between the environmental conditions and the view of
landscapes from the Sentinel-2 satellite. We trained a conditional generative
adversarial network to generate multispectral imagery given a set of climatic,
terrain and anthropogenic predictors. The generated imagery of the landscapes
share many characteristics with the real one. Results based on landscape patch
metrics, indicative of landscape composition and structure, show that the
proposed generative model creates landscapes that are more similar to the
targets than the baseline models while overall reflectance and vegetation cover
are predicted better. We demonstrate that for many purposes the generated
landscapes behave as real with immediate application for global change studies.
We envision the application of machine learning as a tool to forecast the
effects of climate change on the spatial features of landscapes, while we
assess its limitations and breaking points.

negative:
Generative adversarial networks (GANs) used in domain adaptation tasks have
the ability to generate images that are both realistic and personalized,
transforming an input image while maintaining its identifiable characteristics.
However, they often require a large quantity of training data to produce
high-quality images in a robust way, which limits their usability in cases when
access to data is limited. In our paper, we explore the potential of using
images from a simulated 3D environment to improve a domain adaptation task
carried out by the MUNIT architecture, aiming to use the resulting images to
raise awareness of the potential future impacts of climate change.

negative:
Device-edge co-inference, which partitions a deep neural network between a
resource-constrained mobile device and an edge server, recently emerges as a
promising paradigm to support intelligent mobile applications. To accelerate
the inference process, on-device model sparsification and intermediate feature
compression are regarded as two prominent techniques. However, as the on-device
model sparsity level and intermediate feature compression ratio have direct
impacts on computation workload and communication overhead respectively, and
both of them affect the inference accuracy, finding the optimal values of these
hyper-parameters brings a major challenge due to the large search space. In
this paper, we endeavor to develop an efficient algorithm to determine these
hyper-parameters. By selecting a suitable model split point and a pair of
encoder/decoder for the intermediate feature vector, this problem is casted as
a sequential decision problem, for which, a novel automated machine learning
(AutoML) framework is proposed based on deep reinforcement learning (DRL).
Experiment results on an image classification task demonstrate the
effectiveness of the proposed framework in achieving a better
communication-computation trade-off and significant inference speedup against
various baseline schemes.

negative:
Segmentation of additive manufacturing (AM) defects in X-ray Computed
Tomography (XCT) images is challenging, due to the poor contrast, small sizes
and variation in appearance of defects. Automatic segmentation can, however,
provide quality control for additive manufacturing. Over recent years,
three-dimensional convolutional neural networks (3D CNNs) have performed well
in the volumetric segmentation of medical images. In this work, we leverage
techniques from the medical imaging domain and propose training a 3D U-Net
model to automatically segment defects in XCT images of AM samples. This work
not only contributes to the use of machine learning for AM defect detection but
also demonstrates for the first time 3D volumetric segmentation in AM. We train
and test with three variants of the 3D U-Net on an AM dataset, achieving a mean
intersection of union (IOU) value of 88.4%.

negative:
As climate change increases the intensity of natural disasters, society needs
better tools for adaptation. Floods, for example, are the most frequent natural
disaster, and better tools for flood risk communication could increase the
support for flood-resilient infrastructure development. Our work aims to enable
more visual communication of large-scale climate impacts via visualizing the
output of coastal flood models as satellite imagery. We propose the first deep
learning pipeline to ensure physical-consistency in synthetic visual satellite
imagery. We advanced a state-of-the-art GAN called pix2pixHD, such that it
produces imagery that is physically-consistent with the output of an
expert-validated storm surge model (NOAA SLOSH). By evaluating the imagery
relative to physics-based flood maps, we find that our proposed framework
outperforms baseline models in both physical-consistency and photorealism. We
envision our work to be the first step towards a global visualization of how
the climate challenge will shape our landscape. Continuing on this path, we
show that the proposed pipeline generalizes to visualize reforestation. We also
publish a dataset of over 25k labelled image-triplets to study image-to-image
translation in Earth observation.

negative:
Development and diffusion of machine learning and big data tools provide a
new tool for architects and urban planners that could be used as analytical or
design instruments. The topic investigated in this paper is the application of
Generative Adversarial Networks to the design of an urban block. The research
presents a flexible model able to adapt to the morphological characteristics of
a city. This method does not define explicitly any of the parameters of an
urban block typical for a city, the algorithm learns them from the existing
urban context. This approach has been applied to the cities with different
morphology: Milan, Amsterdam, Tallinn, Turin, and Bengaluru in order to see the
performance of the model and the possibility of style translation between
different cities. The data are gathered from Openstreetmap and Open Data
portals of the cities. This research presents the results of the experiments
and their quantitative and qualitative evaluation.

negative:
The deep generative adversarial networks (GAN) recently have been shown to be
promising for different computer vision applications, like image edit- ing,
synthesizing high resolution images, generating videos, etc. These networks and
the corresponding learning scheme can handle various visual space map- pings.
We approach GANs with a novel training method and learning objective, to
discover multiple object instances for three cases: 1) synthesizing a picture
of a specific object within a cluttered scene; 2) localizing different
categories in images for weakly supervised object detection; and 3) improving
object discov- ery in object detection pipelines. A crucial advantage of our
method is that it learns a new deep similarity metric, to distinguish multiple
objects in one im- age. We demonstrate that the network can act as an
encoder-decoder generating parts of an image which contain an object, or as a
modified deep CNN to rep- resent images for object detection in supervised and
weakly supervised scheme. Our ranking GAN offers a novel way to search through
images for object specific patterns. We have conducted experiments for
different scenarios and demonstrate the method performance for object
synthesizing and weakly supervised object detection and classification using
the MS-COCO and PASCAL VOC datasets.

negative:
One of the consequences of climate change is anobserved increase in the
frequency of extreme cli-mate events. That poses a challenge for
weatherforecast and generation algorithms, which learnfrom historical data but
should embed an often un-certain bias to create correct scenarios. This
paperinvestigates how mapping climate data to a knowndistribution using
variational autoencoders mighthelp explore such biases and control the
synthesisof weather fields towards more extreme climatescenarios. We
experimented using a monsoon-affected precipitation dataset from southwest
In-dia, which should give a roughly stable pattern ofrainy days and ease our
investigation. We reportcompelling results showing that mapping complexweather
data to a known distribution implementsan efficient control for weather field
synthesis to-wards more (or less) extreme scenarios.

negative:
Projection of changes in extreme indices of climate variables such as
temperature and precipitation are critical to assess the potential impacts of
climate change on human-made and natural systems, including critical
infrastructures and ecosystems. While impact assessment and adaptation planning
rely on high-resolution projections (typically in the order of a few
kilometers), state-of-the-art Earth System Models (ESMs) are available at
spatial resolutions of few hundreds of kilometers. Current solutions to obtain
high-resolution projections of ESMs include downscaling approaches that
consider the information at a coarse-scale to make predictions at local scales.
Complex and non-linear interdependence among local climate variables (e.g.,
temperature and precipitation) and large-scale predictors (e.g., pressure
fields) motivate the use of neural network-based super-resolution
architectures. In this work, we present auxiliary variables informed
spatio-temporal neural architecture for statistical downscaling. The current
study performs daily downscaling of precipitation variable from an ESM output
at 1.15 degrees (~115 km) to 0.25 degrees (25 km) over the world's most
climatically diversified country, India. We showcase significant improvement
gain against three popular state-of-the-art baselines with a better ability to
predict extreme events. To facilitate reproducible research, we make available
all the codes, processed datasets, and trained models in the public domain.

negative:
The demand for on-device document recognition systems increases in
conjunction with the emergence of more strict privacy and security
requirements. In such systems, there is no data transfer from the end device to
a third-party information processing servers. The response time is vital to the
user experience of on-device document recognition. Combined with the
unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on
consumer-grade end devices such as smartphones, the time limitations put
significant constraints on the computational complexity of the applied
algorithms for on-device execution.
  In this work, we consider document location in an image without prior
knowledge of the document content or its internal structure. In accordance with
the published works, at least 5 systems offer solutions for on-device document
location. All these systems use a location method which can be considered
Hough-based. The precision of such systems seems to be lower than that of the
state-of-the-art solutions which were not designed to account for the limited
computational resources.
  We propose an advanced Hough-based method. In contrast with other approaches,
it accounts for the geometric invariants of the central projection model and
combines both edge and color features for document boundary detection. The
proposed method allowed for the second best result for SmartDoc dataset in
terms of precision, surpassed by U-net like neural network. When evaluated on a
more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best
precision compared to published methods. Our method retained the applicability
to on-device computations.

negative:
Understanding 3D object structure from a single image is an important but
difficult task in computer vision, mostly due to the lack of 3D object
annotations in real images. Previous work tackles this problem by either
solving an optimization task given 2D keypoint positions, or training on
synthetic data with ground truth 3D information. In this work, we propose 3D
INterpreter Network (3D-INN), an end-to-end framework which sequentially
estimates 2D keypoint heatmaps and 3D object structure, trained on both real
2D-annotated images and synthetic 3D data. This is made possible mainly by two
technical innovations. First, we propose a Projection Layer, which projects
estimated 3D structure to 2D space, so that 3D-INN can be trained to predict 3D
structural parameters supervised by 2D annotations on real images. Second,
heatmaps of keypoints serve as an intermediate representation connecting real
and synthetic data, enabling 3D-INN to benefit from the variation and abundance
of synthetic 3D objects, without suffering from the difference between the
statistics of real and synthesized images due to imperfect rendering. The
network achieves state-of-the-art performance on both 2D keypoint estimation
and 3D structure recovery. We also show that the recovered 3D information can
be used in other vision applications, such as 3D rendering and image retrieval.

negative:
The effects of global climate change on Peruvian glaciers have brought about
several processes of deglaciation during the last few years. The immediate
effect is the change of size of lakes and rivers. Public institutions that
monitor water resources currently have only recent studies which make up less
than 10% of the total. The effects of climate change and the lack of updated
information intensify social-economic problems related to water resources in
Peru. The objective of this research is to develop a software application to
automate the Cadastral Registry of Water Bodies in Peru, using techniques of
digital image processing, which would provide tools for detection, record,
temporal analysis and visualization of water bodies. The images used are from
the satellite Landsat5, which undergo a pre-processing of calibration and
correction of the satellite. Detection results are archived into a file that
contains location vectors and images of the segmentated bodies of water.

negative:
Deep learning models are being deployed in many mobile intelligent
applications. End-side services, such as intelligent personal assistants,
autonomous cars, and smart home services often employ either simple local
models on the mobile or complex remote models on the cloud. However, recent
studies have shown that partitioning the DNN computations between the mobile
and cloud can increase the latency and energy efficiencies. In this paper, we
propose an efficient, adaptive, and practical engine, JointDNN, for
collaborative computation between a mobile device and cloud for DNNs in both
inference and training phase. JointDNN not only provides an energy and
performance efficient method of querying DNNs for the mobile side but also
benefits the cloud server by reducing the amount of its workload and
communications compared to the cloud-only approach. Given the DNN architecture,
we investigate the efficiency of processing some layers on the mobile device
and some layers on the cloud server. We provide optimization formulations at
layer granularity for forward- and backward-propagations in DNNs, which can
adapt to mobile battery limitations and cloud server load constraints and
quality of service. JointDNN achieves up to 18 and 32 times reductions on the
latency and mobile energy consumption of querying DNNs compared to the
status-quo approaches, respectively.

negative:
The biggest challenge faced by a Machine Learning Engineer is the lack of
data they have, especially for 2-dimensional images. The image is processed to
be trained into a Machine Learning model so that it can recognize patterns in
the data and provide predictions. This research is intended to create a
solution using the Cycle Generative Adversarial Networks (GANs) algorithm in
overcoming the problem of lack of data. Then use Style Transfer to be able to
generate a new image based on the given style. Based on the results of testing
the resulting model has been carried out several improvements, previously the
loss value of the photo generator: 3.1267, monet style generator: 3.2026, photo
discriminator: 0.6325, and monet style discriminator: 0.6931 to photo
generator: 2.3792, monet style generator: 2.7291, photo discriminator: 0.5956,
and monet style discriminator: 0.4940. It is hoped that the research will make
the application of this solution useful in the fields of Education, Arts,
Information Technology, Medicine, Astronomy, Automotive and other important
fields.

negative:
Automatically generating maps from satellite images is an important task.
There is a body of literature which tries to address this challenge. We created
a more expansive survey of the task by experimenting with different models and
adding new loss functions to improve results. We created a database of pairs of
satellite images and the corresponding map of the area. Our model translates
the satellite image to the corresponding standard layer map image using three
main model architectures: (i) a conditional Generative Adversarial Network
(GAN) which compresses the images down to a learned embedding, (ii) a generator
which is trained as a normalizing flow (RealNVP) model, and (iii) a conditional
GAN where the generator translates via a series of convolutions to the standard
layer of a map and the discriminator input is the concatenation of the
real/generated map and the satellite image. Model (iii) was by far the most
promising of three models. To improve the results we also added a
reconstruction loss and style transfer loss in addition to the GAN losses. The
third model architecture produced the best quality of sampled images. In
contrast to the other generative model where evaluation of the model is a
challenging problem. since we have access to the real map for a given satellite
image, we are able to assign a quantitative metric to the quality of the
generated images in addition to inspecting them visually. While we are
continuing to work on increasing the accuracy of the model, one challenge has
been the coarse resolution of the data which upper-bounds the quality of the
results of our model. Nevertheless, as will be seen in the results, the
generated map is more accurate in the features it produces since the generator
architecture demands a pixel-wise image translation/pixel-wise coloring. A
video presentation summarizing this paper is available at:
https://youtu.be/Ur0flOX-Ji0

negative:
This paper introduces a conditional generative adversarial network to
redesign a street-level image of urban scenes by generating 1) an urban
intervention policy, 2) an attention map that localises where intervention is
needed, 3) a high-resolution street-level image (1024 X 1024 or 1536 X1536)
after implementing the intervention. We also introduce a new dataset that
comprises aligned street-level images of before and after urban interventions
from real-life scenarios that make this research possible. The introduced
method has been trained on different ranges of urban interventions applied to
realistic images. The trained model shows strong performance in re-modelling
cities, outperforming existing methods that apply image-to-image translation in
other domains that is computed in a single GPU. This research opens the door
for machine intelligence to play a role in re-thinking and re-designing the
different attributes of cities based on adversarial learning, going beyond the
mainstream of facial landmarks manipulation or image synthesis from semantic
segmentation.

negative:
There are many applications of Generative Adversarial Networks (GANs) in
fields like computer vision, natural language processing, speech synthesis, and
more. Undoubtedly the most notable results have been in the area of image
synthesis and in particular in the generation of deepfake videos. While
deepfakes have received much negative media coverage, they can be a useful
technology in applications like entertainment, customer relations, or even
assistive care. One problem with generating deepfakes is the requirement for a
lot of image training data of the subject which is not an issue if the subject
is a celebrity for whom many images already exist. If there are only a small
number of training images then the quality of the deepfake will be poor. Some
media reports have indicated that a good deepfake can be produced with as few
as 500 images but in practice, quality deepfakes require many thousands of
images, one of the reasons why deepfakes of celebrities and politicians have
become so popular. In this study, we exploit the property of a GAN to produce
images of an individual with variable facial expressions which we then use to
generate a deepfake. We observe that with such variability in facial
expressions of synthetic GAN-generated training images and a reduced quantity
of them, we can produce a near-realistic deepfake videos.

negative:
"If I provide you a face image of mine (without telling you the actual age
when I took the picture) and a large amount of face images that I crawled
(containing labeled faces of different ages but not necessarily paired), can
you show me what I would look like when I am 80 or what I was like when I was
5?" The answer is probably a "No." Most existing face aging works attempt to
learn the transformation between age groups and thus would require the paired
samples as well as the labeled query image. In this paper, we look at the
problem from a generative modeling perspective such that no paired samples is
required. In addition, given an unlabeled image, the generative model can
directly produce the image with desired age attribute. We propose a conditional
adversarial autoencoder (CAAE) that learns a face manifold, traversing on which
smooth age progression and regression can be realized simultaneously. In CAAE,
the face is first mapped to a latent vector through a convolutional encoder,
and then the vector is projected to the face manifold conditional on age
through a deconvolutional generator. The latent vector preserves personalized
face features (i.e., personality) and the age condition controls progression
vs. regression. Two adversarial networks are imposed on the encoder and
generator, respectively, forcing to generate more photo-realistic faces.
Experimental results demonstrate the appealing performance and flexibility of
the proposed framework by comparing with the state-of-the-art and ground truth.

negative:
The edge computing paradigm places compute-capable devices - edge servers -
at the network edge to assist mobile devices in executing data analysis tasks.
Intuitively, offloading compute-intense tasks to edge servers can reduce their
execution time. However, poor conditions of the wireless channel connecting the
mobile devices to the edge servers may degrade the overall capture-to-output
delay achieved by edge offloading. Herein, we focus on edge computing
supporting remote object detection by means of Deep Neural Networks (DNNs), and
develop a framework to reduce the amount of data transmitted over the wireless
link. The core idea we propose builds on recent approaches splitting DNNs into
sections - namely head and tail models - executed by the mobile device and edge
server, respectively. The wireless link, then, is used to transport the output
of the last layer of the head model to the edge server, instead of the DNN
input. Most prior work focuses on classification tasks and leaves the DNN
structure unaltered. Herein, our focus is on DNNs for three different object
detection tasks, which present a much more convoluted structure, and modify the
architecture of the network to: (i) achieve in-network compression by
introducing a bottleneck layer in the early layers on the head model, and (ii)
prefilter pictures that do not contain objects of interest using a
convolutional neural network. Results show that the proposed technique
represents an effective intermediate option between local and edge computing in
a parameter region where these extreme point solutions fail to provide
satisfactory performance. The code and trained models are available at
https://github.com/yoshitomo-matsubara/hnd-ghnd-object-detectors .

negative:
We consider the task of photo-realistic unconditional image generation
(generate high quality, diverse samples that carry the same visual content as
the image) on mobile platforms using Generative Adversarial Networks (GANs). In
this paper, we propose a novel approach to trade-off image generation accuracy
of a GAN for the energy consumed (compute) at run-time called Scale-Energy
Tradeoff GAN (SETGAN). GANs usually take a long time to train and consume a
huge memory hence making it difficult to run on edge devices. The key idea
behind SETGAN for an image generation task is for a given input image, we train
a GAN on a remote server and use the trained model on edge devices. We use
SinGAN, a single image unconditional generative model, that contains a pyramid
of fully convolutional GANs, each responsible for learning the patch
distribution at a different scale of the image. During the training process, we
determine the optimal number of scales for a given input image and the energy
constraint from the target edge device. Results show that with SETGAN's unique
client-server-based architecture, we were able to achieve a 56% gain in energy
for a loss of 3% to 12% SSIM accuracy. Also, with the parallel multi-scale
training, we obtain around 4x gain in training time on the server.

negative:
We present EgoRenderer, a system for rendering full-body neural avatars of a
person captured by a wearable, egocentric fisheye camera that is mounted on a
cap or a VR headset. Our system renders photorealistic novel views of the actor
and her motion from arbitrary virtual camera locations. Rendering full-body
avatars from such egocentric images come with unique challenges due to the
top-down view and large distortions. We tackle these challenges by decomposing
the rendering process into several steps, including texture synthesis, pose
construction, and neural image translation. For texture synthesis, we propose
Ego-DPNet, a neural network that infers dense correspondences between the input
fisheye images and an underlying parametric body model, and to extract textures
from egocentric inputs. In addition, to encode dynamic appearances, our
approach also learns an implicit texture stack that captures detailed
appearance variation across poses and viewpoints. For correct pose generation,
we first estimate body pose from the egocentric view using a parametric model.
We then synthesize an external free-viewpoint pose image by projecting the
parametric model to the user-specified target viewpoint. We next combine the
target pose image and the textures into a combined feature image, which is
transformed into the output color image using a neural image translation
network. Experimental evaluations show that EgoRenderer is capable of
generating realistic free-viewpoint avatars of a person wearing an egocentric
camera. Comparisons to several baselines demonstrate the advantages of our
approach.

negative:
We introduce a novel framework to build a model that can learn how to segment
objects from a collection of images without any human annotation. Our method
builds on the observation that the location of object segments can be perturbed
locally relative to a given background without affecting the realism of a
scene. Our approach is to first train a generative model of a layered scene.
The layered representation consists of a background image, a foreground image
and the mask of the foreground. A composite image is then obtained by
overlaying the masked foreground image onto the background. The generative
model is trained in an adversarial fashion against a discriminator, which
forces the generative model to produce realistic composite images. To force the
generator to learn a representation where the foreground layer corresponds to
an object, we perturb the output of the generative model by introducing a
random shift of both the foreground image and mask relative to the background.
Because the generator is unaware of the shift before computing its output, it
must produce layered representations that are realistic for any such random
perturbation. Finally, we learn to segment an image by defining an autoencoder
consisting of an encoder, which we train, and the pre-trained generator as the
decoder, which we freeze. The encoder maps an image to a feature vector, which
is fed as input to the generator to give a composite image matching the
original input image. Because the generator outputs an explicit layered
representation of the scene, the encoder learns to detect and segment objects.
We demonstrate this framework on real images of several object categories.

negative:
Recent approaches in literature have exploited the multi-modal information in
documents (text, layout, image) to serve specific downstream document tasks.
However, they are limited by their - (i) inability to learn cross-modal
representations across text, layout and image dimensions for documents and (ii)
inability to process multi-page documents. Pre-training techniques have been
shown in Natural Language Processing (NLP) domain to learn generic textual
representations from large unlabelled datasets, applicable to various
downstream NLP tasks. In this paper, we propose a multi-task learning-based
framework that utilizes a combination of self-supervised and supervised
pre-training tasks to learn a generic document representation applicable to
various downstream document tasks. Specifically, we introduce Document Topic
Modelling and Document Shuffle Prediction as novel pre-training tasks to learn
rich image representations along with the text and layout representations for
documents. We utilize the Longformer network architecture as the backbone to
encode the multi-modal information from multi-page documents in an end-to-end
fashion. We showcase the applicability of our pre-training framework on a
variety of different real-world document tasks such as document classification,
document information extraction, and document retrieval. We evaluate our
framework on different standard document datasets and conduct exhaustive
experiments to compare performance against various ablations of our framework
and state-of-the-art baselines.

negative:
High user interaction capability of mobile devices can help improve the
accuracy of mobile visual search systems. At query time, it is possible to
capture multiple views of an object from different viewing angles and at
different scales with the mobile device camera to obtain richer information
about the object compared to a single view and hence return more accurate
results. Motivated by this, we developed a mobile multi-view object image
search system, using a client-server architecture. Multi-view images of objects
acquired by the mobile clients are processed and local features are sent to the
server, which combines the query image representations with early/late fusion
methods based on bag-of-visual-words and sends back the query results. We
performed a comprehensive analysis of early and late fusion approaches using
various similarity functions, on an existing single view and a new multi-view
object image database. The experimental results show that multi-view search
provides significantly better retrieval accuracy compared to single view
search.

negative:
Reconstructing the 3D geometry of an object from an image is a major
challenge in computer vision. Recently introduced differentiable renderers can
be leveraged to learn the 3D geometry of objects from 2D images, but those
approaches require additional supervision to enable the renderer to produce an
output that can be compared to the input image. This can be scene information
or constraints such as object silhouettes, uniform backgrounds, material,
texture, and lighting. In this paper, we propose an approach that enables a
differentiable rendering-based learning of 3D objects from images with
backgrounds without the need for silhouette supervision. Instead of trying to
render an image close to the input, we propose an adversarial style-transfer
and domain adaptation pipeline that allows to translate the input image domain
to the rendered image domain. This allows us to directly compare between a
translated image and the differentiable rendering of a 3D object reconstruction
in order to train the 3D object reconstruction network. We show that the
approach learns 3D geometry from images with backgrounds and provides a better
performance than constrained methods for single-view 3D object reconstruction
on this task.

negative:
Climate change has become one of the biggest global problems increasingly
compromising the Earth's habitability. Recent developments such as the
extraordinary heat waves in California & Canada, and the devastating floods in
Germany point to the role of climate change in the ever-increasing frequency of
extreme weather. Numerical modelling of the weather and climate have seen
tremendous improvements in the last five decades, yet stringent limitations
remain to be overcome. Spatially and temporally localized forecasting is the
need of the hour for effective adaptation measures towards minimizing the loss
of life and property. Artificial Intelligence-based methods are demonstrating
promising results in improving predictions, but are still limited by the
availability of requisite hardware and software required to process the vast
deluge of data at a scale of the planet Earth. Quantum computing is an emerging
paradigm that has found potential applicability in several fields. In this
opinion piece, we argue that new developments in Artificial Intelligence
algorithms designed for quantum computers - also known as Quantum Artificial
Intelligence (QAI) - may provide the key breakthroughs necessary to furthering
the science of climate change. The resultant improvements in weather and
climate forecasts are expected to cascade to numerous societal benefits.

negative:
Climate change is expected to reshuffle the settlement landscape: forcing
people in affected areas to migrate, to change their lifeways, and continuing
to affect demographic change throughout the world. Changes to the geographic
distribution of population will have dramatic impacts on land use and land
cover and thus constitute one of the major challenges of planning for climate
change scenarios. In this paper, we explore a generative model framework for
generating satellite imagery conditional on gridded population distributions.
We make additions to the existing ALAE architecture, creating a spatially
conditional version: SCALAE. This method allows us to explicitly disentangle
population from the model's latent space and thus input custom population
forecasts into the generated imagery. We postulate that such imagery could then
be directly used for land cover and land use change estimation using existing
frameworks, as well as for realistic visualisation of expected local change. We
evaluate the model by comparing pixel and semantic reconstructions, as well as
calculate the standard FID metric. The results suggest the model captures
population distributions accurately and delivers a controllable method to
generate realistic satellite imagery.

negative:
Astronomy of the 21st century increasingly finds itself with extreme
quantities of data. This growth in data is ripe for modern technologies such as
deep image processing, which has the potential to allow astronomers to
automatically identify, classify, segment and deblend various astronomical
objects. In this paper, we explore the use of chained generative adversarial
networks (GANs), a class of generative models that learn mappings from latent
spaces to data distributions by modelling the joint distribution of the data,
to produce physically realistic galaxy images as one use case of such models.
In cosmology, such datasets can aid in the calibration of shape measurements
for weak lensing by augmenting data with synthetic images. By measuring the
distributions of multiple physical properties, we show that images generated
with our approach closely follow the distributions of real galaxies, further
establishing state-of-the-art GAN architectures as a valuable tool for
modern-day astronomy.

negative:
The difficulty in obtaining labeled data relevant to a given task is among
the most common and well-known practical obstacles to applying deep learning
techniques to new or even slightly modified domains. The data volumes required
by the current generation of supervised learning algorithms typically far
exceed what a human needs to learn and complete a given task. We investigate
ways to expand a given labeled corpus of remote sensed imagery into a larger
corpus using Generative Adversarial Networks (GANs). We then measure how these
additional synthetic data affect supervised machine learning performance on an
object detection task.
  Our data driven strategy is to train GANs to (1) generate synthetic
segmentation masks and (2) generate plausible synthetic remote sensing imagery
corresponding to these segmentation masks. Run sequentially, these GANs allow
the generation of synthetic remote sensing imagery complete with segmentation
labels. We apply this strategy to the data set from ISPRS' 2D Semantic Labeling
Contest - Potsdam, with a follow on vehicle detection task. We find that in
scenarios with limited training data, augmenting the available data with such
synthetically generated data can improve detector performance.

negative:
In medical imaging, a general problem is that it is costly and time consuming
to collect high quality data from healthy and diseased subjects. Generative
adversarial networks (GANs) is a deep learning method that has been developed
for synthesizing data. GANs can thereby be used to generate more realistic
training data, to improve classification performance of machine learning
algorithms. Another application of GANs is image-to-image translations, e.g.
generating magnetic resonance (MR) images from computed tomography (CT) images,
which can be used to obtain multimodal datasets from a single modality. Here,
we evaluate two unsupervised GAN models (CycleGAN and UNIT) for image-to-image
translation of T1- and T2-weighted MR images, by comparing generated synthetic
MR images to ground truth images. We also evaluate two supervised models; a
modification of CycleGAN and a pure generator model. A small perceptual study
was also performed to evaluate how visually realistic the synthesized images
are. It is shown that the implemented GAN models can synthesize visually
realistic MR images (incorrectly labeled as real by a human). It is also shown
that models producing more visually realistic synthetic images not necessarily
have better quantitative error measurements, when compared to ground truth
data. Code is available at https://github.com/simontomaskarlsson/GAN-MRI

negative:
Climate change has been a common interest and the forefront of crucial
political discussion and decision-making for many years. Shallow clouds play a
significant role in understanding the Earth's climate, but they are challenging
to interpret and represent in a climate model. By classifying these cloud
structures, there is a better possibility of understanding the physical
structures of the clouds, which would improve the climate model generation,
resulting in a better prediction of climate change or forecasting weather
update. Clouds organise in many forms, which makes it challenging to build
traditional rule-based algorithms to separate cloud features. In this paper,
classification of cloud organization patterns was performed using a new
scaled-up version of Convolutional Neural Network (CNN) named as EfficientNet
as the encoder and UNet as decoder where they worked as feature extractor and
reconstructor of fine grained feature map and was used as a classifier, which
will help experts to understand how clouds will shape the future climate. By
using a segmentation model in a classification task, it was shown that with a
good encoder alongside UNet, it is possible to obtain good performance from
this dataset. Dice coefficient has been used for the final evaluation metric,
which gave the score of 66.26\% and 66.02\% for public and private (test set)
leaderboard on Kaggle competition respectively.

negative:
Faces and their expressions are one of the potent subjects for digital
images. Detecting emotions from images is an ancient task in the field of
computer vision; however, performing its reverse -- synthesizing facial
expressions from images -- is quite new. Such operations of regenerating images
with different facial expressions, or altering an existing expression in an
image require the Generative Adversarial Network (GAN). In this paper, we aim
to change the facial expression in an image using GAN, where the input image
with an initial expression (i.e., happy) is altered to a different expression
(i.e., disgusted) for the same person. We used StarGAN techniques on a modified
version of the MUG dataset to accomplish this objective. Moreover, we extended
our work further by remodeling facial expressions in an image indicated by the
emotion from a given text. As a result, we applied a Long Short-Term Memory
(LSTM) method to extract emotion from the text and forwarded it to our
expression-altering module. As a demonstration of our working pipeline, we also
create an application prototype of a blog that regenerates the profile picture
with different expressions based on the user's textual emotion.

negative:
We introduce CLIMATE-FEVER, a new publicly available dataset for verification
of climate change-related claims. By providing a dataset for the research
community, we aim to facilitate and encourage work on improving algorithms for
retrieving evidential support for climate-specific claims, addressing the
underlying language understanding challenges, and ultimately help alleviate the
impact of misinformation on climate change. We adapt the methodology of FEVER
[1], the largest dataset of artificially designed claims, to real-life claims
collected from the Internet. While during this process, we could rely on the
expertise of renowned climate scientists, it turned out to be no easy task. We
discuss the surprising, subtle complexity of modeling real-world
climate-related claims within the \textsc{fever} framework, which we believe
provides a valuable challenge for general natural language understanding. We
hope that our work will mark the beginning of a new exciting long-term joint
effort by the climate science and AI community.

negative:
Cloud computing offers the potential to help scientists to process massive
number of computing resources often required in machine learning application
such as computer vision problems. This proposal would like to show that which
benefits can be obtained from cloud in order to help medical image analysis
users (including scientists, clinicians, and research institutes). As security
and privacy of algorithms are important for most of algorithms inventors, these
algorithms can be hidden in a cloud to allow the users to use the algorithms as
a package without any access to see/change their inside. In another word, in
the user part, users send their images to the cloud and configure the algorithm
via an interface. In the cloud part, the algorithms are applied to this image
and the results are returned back to the user. My proposal has two parts: (1)
investigate the potential of cloud computing for computer vision problems and
(2) study the components of a proposed cloud-based framework for medical image
analysis application and develop them (depending on the length of the
internship). The investigation part will involve a study on several aspects of
the problem including security, usability (for medical end users of the
service), appropriate programming abstractions for vision problems, scalability
and resource requirements. In the second part of this proposal I am going to
thoroughly study of the proposed framework components and their relations and
develop them. The proposed cloud-based framework includes an integrated
environment to enable scientists and clinicians to access to the previous and
current medical image analysis algorithms using a handful user interface
without any access to the algorithm codes and procedures.

negative:
Climate change communication in the mass media and other textual sources may
affect and shape public perception. Extracting climate change information from
these sources is an important task, e.g., for filtering content and
e-discovery, sentiment analysis, automatic summarization, question-answering,
and fact-checking. However, automating this process is a challenge, as climate
change is a complex, fast-moving, and often ambiguous topic with scarce
resources for popular text-based AI tasks. In this paper, we introduce
\textsc{ClimaText}, a dataset for sentence-based climate change topic
detection, which we make publicly available. We explore different approaches to
identify the climate change topic in various text sources. We find that popular
keyword-based models are not adequate for such a complex and evolving task.
Context-based algorithms like BERT \cite{devlin2018bert} can detect, in
addition to many trivial cases, a variety of complex and implicit topic
patterns. Nevertheless, our analysis reveals a great potential for improvement
in several directions, such as, e.g., capturing the discussion on indirect
effects of climate change. Hence, we hope this work can serve as a good
starting point for further research on this topic.

negative:
In this technical report I present my method for automatic synthetic dataset
generation for object detection and demonstrate it on the video game League of
Legends. This report furthermore serves as a handbook on how to automatically
generate datasets and as an introduction on the dataset generation part of the
LeagueAI framework. The LeagueAI framework is a software framework that
provides detailed information about the game League of Legends based on the
same input a human player would have, namely vision. The framework allows
researchers and enthusiasts to develop their own intelligent agents or to
extract detailed information about the state of the game. A big problem of
machine vision applications usually is the laborious work of gathering large
amounts of hand labeled data. Thus, a crucial part of the vision pipeline of
the LeagueAI framework, the dataset generation, is presented in this report.
The method involves extracting image raw data from the game's 3D models and
combining them with the game background to create game-like synthetic images
and to generate the corresponding labels automatically. In an experiment I
compared a model trained on synthetic data to a model trained on hand labeled
data and a model trained on a combined dataset. The model trained on the
synthetic data showed higher detection precision on more classes and more
reliable tracking performance of the player character. The model trained on the
combined dataset did not perform better because of the different formats of the
older hand labeled dataset and the synthetic data.

negative:
The ever-growing popularity and rapid improving of artificial intelligence
(AI) have raised rethinking on the evolution of wireless networks. Mobile edge
computing (MEC) provides a natural platform for AI applications since it is
with rich computation resources to train machine learning (ML) models, as well
as low-latency access to the data generated by mobile and internet of things
(IoT) devices. In this paper, we present an infrastructure to perform ML tasks
at an MEC server with the assistance of a reconfigurable intelligent surface
(RIS). In contrast to conventional communication systems where the principal
criterions are to maximize the throughput, we aim at maximizing the learning
performance. Specifically, we minimize the maximum learning error of all
participating users by jointly optimizing transmit power of mobile users,
beamforming vectors of the base station (BS), and the phase-shift matrix of the
RIS. An alternating optimization (AO)-based framework is proposed to optimize
the three terms iteratively, where a successive convex approximation
(SCA)-based algorithm is developed to solve the power allocation problem,
closed-form expressions of the beamforming vectors are derived, and an
alternating direction method of multipliers (ADMM)-based algorithm is designed
together with an error level searching (ELS) framework to effectively solve the
challenging nonconvex optimization problem of the phase-shift matrix.
Simulation results demonstrate significant gains of deploying an RIS and
validate the advantages of our proposed algorithms over various benchmarks.
Lastly, a unified communication-training-inference platform is developed based
on the CARLA platform and the SECOND network, and a use case (3D object
detection in autonomous driving) for the proposed scheme is demonstrated on the
developed platform.

negative:
In the big data era, the impetus to digitize the vast reservoirs of data
trapped in unstructured scanned documents such as invoices, bank documents and
courier receipts has gained fresh momentum. The scanning process often results
in the introduction of artifacts such as background noise, blur due to camera
motion, watermarkings, coffee stains, or faded text. These artifacts pose many
readability challenges to current text recognition algorithms and significantly
degrade their performance. Existing learning based denoising techniques require
a dataset comprising of noisy documents paired with cleaned versions. In such
scenarios, a model can be trained to generate clean documents from noisy
versions. However, very often in the real world such a paired dataset is not
available, and all we have for training our denoising model are unpaired sets
of noisy and clean images. This paper explores the use of GANs to generate
denoised versions of the noisy documents. In particular, where paired
information is available, we formulate the problem as an image-to-image
translation task i.e, translating a document from noisy domain ( i.e.,
background noise, blurred, faded, watermarked ) to a target clean document
using Generative Adversarial Networks (GAN). However, in the absence of paired
images for training, we employed CycleGAN which is known to learn a mapping
between the distributions of the noisy images to the denoised images using
unpaired data to achieve image-to-image translation for cleaning the noisy
documents. We compare the performance of CycleGAN for document cleaning tasks
using unpaired images with a Conditional GAN trained on paired data from the
same dataset. Experiments were performed on a public document dataset on which
different types of noise were artificially induced, results demonstrate that
CycleGAN learns a more robust mapping from the space of noisy to clean
documents.

negative:
Low-quality face image restoration is a popular research direction in today's
computer vision field. It can be used as a pre-work for tasks such as face
detection and face recognition. At present, there is a lot of work to solve the
problem of low-quality faces under various environmental conditions. This paper
mainly focuses on the restoration of motion-blurred faces. In increasingly
abundant mobile scenes, the fast recovery of motion-blurred faces can bring
highly effective speed improvements in tasks such as face matching. In order to
achieve this goal, a deblurring method for motion-blurred facial image signals
based on generative adversarial networks(GANs) is proposed. It uses an
end-to-end method to train a sharp image generator, i.e., a processor for
motion-blurred facial images. This paper introduce the processing progress of
motion-blurred images, the development and changes of GANs and some basic
concepts. After that, it give the details of network structure and training
optimization design of the image processor. Then we conducted a motion blur
image generation experiment on some general facial data set, and used the pairs
of blurred and sharp face image data to perform the training and testing
experiments of the processor GAN, and gave some visual displays. Finally, MTCNN
is used to detect the faces of the image generated by the deblurring processor,
and compare it with the result of the blurred image. From the results, the
processing effect of the deblurring processor on the motion-blurred picture has
a significant improvement both in terms of intuition and evaluation indicators
of face detection.

negative:
A growing number of commercially available mobile phones come with integrated
high-resolution digital cameras. That enables a new class of dedicated
applications to image analysis such as mobile visual search, image cropping,
object detection, content-based image retrieval, image classification. In this
paper, a new mobile application for image content retrieval and classification
for mobile device display is proposed to enrich the visual experience of users.
The mobile application can extract a certain number of images based on the
content of an image with visual saliency methods aiming at detecting the most
critical regions in a given image from a perceptual viewpoint. First, the most
critical areas from a perceptual perspective are extracted using the local
maxima of a 2D saliency function. Next, a salient region is cropped using the
bounding box centred on the local maxima of the thresholded Saliency Map of the
image. Then, each image crop feds into an Image Classification system based on
SVM and SIFT descriptors to detect the class of object present in the image.
ImageNet repository was used as the reference for semantic category
classification. Android platform was used to implement the mobile application
on a client-server architecture. A mobile client sends the photo taken by the
camera to the server, which processes the image and returns the results (image
contents such as image crops and related target classes) to the mobile client.
The application was run on thousands of pictures and showed encouraging results
towards a better user visual experience with mobile displays.

negative:
We propose a learning algorithm to design a light-weight neural multiplexer
that given the input and computational resource requirements, calls the model
that will consume the minimum compute resources for a successful inference.
Mobile devices can use the proposed algorithm to offload the hard inputs to the
cloud while inferring the easy ones locally. Besides, in the large scale
cloud-based intelligent applications, instead of replicating the most-accurate
model, a range of small and large models can be multiplexed from depending on
the input's complexity which will save the cloud's computational resources. The
input complexity or hardness is determined by the number of models that can
predict the correct label. For example, if no model can predict the label
correctly, then the input is considered as the hardest. The proposed algorithm
allows the mobile device to detect the inputs that can be processed locally and
the ones that require a larger model and should be sent a cloud server.
Therefore, the mobile user benefits from not only the local processing but also
from an accurate model hosted on a cloud server. Our experimental results show
that the proposed algorithm improves mobile's model accuracy by 8.52% which is
because of those inputs that are properly selected and offloaded to the cloud
server. In addition, it saves the cloud providers' compute resources by a
factor of 2.85x as small models are chosen for easier inputs.

negative:
Despite significant progress on current state-of-the-art image generation
models, synthesis of document images containing multiple and complex object
layouts is a challenging task. This paper presents a novel approach, called
DocSynth, to automatically synthesize document images based on a given layout.
In this work, given a spatial layout (bounding boxes with object categories) as
a reference by the user, our proposed DocSynth model learns to generate a set
of realistic document images consistent with the defined layout. Also, this
framework has been adapted to this work as a superior baseline model for
creating synthetic document image datasets for augmenting real data during
training for document layout analysis tasks. Different sets of learning
objectives have been also used to improve the model performance.
Quantitatively, we also compare the generated results of our model with real
data using standard evaluation metrics. The results highlight that our model
can successfully generate realistic and diverse document images with multiple
objects. We also present a comprehensive qualitative analysis summary of the
different scopes of synthetic image generation tasks. Lastly, to our knowledge
this is the first work of its kind.

negative:
Documents often exhibit various forms of degradation, which make it hard to
be read and substantially deteriorate the performance of an OCR system. In this
paper, we propose an effective end-to-end framework named Document Enhancement
Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs)
to restore severely degraded document images. To the best of our knowledge,
this practice has not been studied within the context of generative adversarial
deep networks. We demonstrate that, in different tasks (document clean up,
binarization, deblurring and watermark removal), DE-GAN can produce an enhanced
version of the degraded document with a high quality. In addition, our approach
provides consistent improvements compared to state-of-the-art methods over the
widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its
ability to restore a degraded document image to its ideal condition. The
obtained results on a wide variety of degradation reveal the flexibility of the
proposed model to be exploited in other document enhancement problems.

negative:
Climate change is a burning issue of our time, with the Sustainable
Development Goal (SDG) 13 of the United Nations demanding global climate
action. Realizing the urgency, in 2015 in Paris, world leaders signed an
agreement committing to taking voluntary action to reduce carbon emissions.
However, the scale, magnitude, and climate action processes vary globally,
especially between developed and developing countries. Therefore, from
parliament to social media, the debates and discussions on climate change
gather data from wide-ranging sources essential to the policy design and
implementation. The downside is that we do not currently have the mechanisms to
pool the worldwide dispersed knowledge emerging from the structured and
unstructured data sources.
  The paper thematically discusses how NLP techniques could be employed in
climate policy research and contribute to society's good at large. In
particular, we exemplify symbiosis of NLP and Climate Policy Research via four
methodologies. The first one deals with the major topics related to climate
policy using automated content analysis. We investigate the opinions
(sentiments) of major actors' narratives towards climate policy in the second
methodology. The third technique explores the climate actors' beliefs towards
pro or anti-climate orientation. Finally, we discuss developing a Climate
Knowledge Graph.
  The present theme paper further argues that creating a knowledge platform
would help in the formulation of a holistic climate policy and effective
climate action. Such a knowledge platform would integrate the policy actors'
varied opinions from different social sectors like government, business, civil
society, and the scientific community. The research outcome will add value to
effective climate action because policymakers can make informed decisions by
looking at the diverse public opinion on a comprehensive platform.

negative:
Machine learning has the potential to aid in mitigating the human effects of
climate change. Previous applications of machine learning to tackle the human
effects in climate change include approaches like informing individuals of
their carbon footprint and strategies to reduce it. For these methods to be the
most effective they must consider relevant social-psychological factors for
each individual. Of social-psychological factors at play in climate change,
affect has been previously identified as a key element in perceptions and
willingness to engage in mitigative behaviours. In this work, we propose an
investigation into how affect could be incorporated to enhance machine learning
based interventions for climate change. We propose using affective agent-based
modelling for climate change as well as the use of a simulated climate change
social dilemma to explore the potential benefits of affective machine learning
interventions. Behavioural and informational interventions can be a powerful
tool in helping humans adopt mitigative behaviours. We expect that utilizing
affective ML can make interventions an even more powerful tool and help
mitigative behaviours become widely adopted.

negative:
With recent progress in graphics, it has become more tractable to train
models on synthetic images, potentially avoiding the need for expensive
annotations. However, learning from synthetic images may not achieve the
desired performance due to a gap between synthetic and real image
distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U)
learning, where the task is to learn a model to improve the realism of a
simulator's output using unlabeled real data, while preserving the annotation
information from the simulator. We develop a method for S+U learning that uses
an adversarial network similar to Generative Adversarial Networks (GANs), but
with synthetic images as inputs instead of random vectors. We make several key
modifications to the standard GAN algorithm to preserve annotations, avoid
artifacts, and stabilize training: (i) a 'self-regularization' term, (ii) a
local adversarial loss, and (iii) updating the discriminator using a history of
refined images. We show that this enables generation of highly realistic
images, which we demonstrate both qualitatively and with a user study. We
quantitatively evaluate the generated images by training models for gaze
estimation and hand pose estimation. We show a significant improvement over
using synthetic images, and achieve state-of-the-art results on the MPIIGaze
dataset without any labeled real data.

negative:
Recent research has made great progress in realizing neural style transfer of
images, which denotes transforming an image to a desired style. Many users
start to use their mobile phones to record their daily life, and then edit and
share the captured images and videos with other users. However, directly
applying existing style transfer approaches on videos, i.e., transferring the
style of a video frame by frame, requires an extremely large amount of
computation resources. It is still technically unaffordable to perform style
transfer of videos on mobile phones. To address this challenge, we propose
MVStylizer, an efficient edge-assisted photorealistic video style transfer
system for mobile phones. Instead of performing stylization frame by frame,
only key frames in the original video are processed by a pre-trained deep
neural network (DNN) on edge servers, while the rest of stylized intermediate
frames are generated by our designed optical-flow-based frame interpolation
algorithm on mobile phones. A meta-smoothing module is also proposed to
simultaneously upscale a stylized frame to arbitrary resolution and remove
style transfer related distortions in these upscaled frames. In addition, for
the sake of continuously enhancing the performance of the DNN model on the edge
server, we adopt a federated learning scheme to keep retraining each DNN model
on the edge server with collected data from mobile clients and syncing with a
global DNN model on the cloud server. Such a scheme effectively leverages the
diversity of collected data from various mobile clients and efficiently
improves the system performance. Our experiments demonstrate that MVStylizer
can generate stylized videos with an even better visual quality compared to the
state-of-the-art method while achieving 75.5$\times$ speedup for
1920$\times$1080 videos.

negative:
Object segmentation is a crucial problem that is usually solved by using
supervised learning approaches over very large datasets composed of both images
and corresponding object masks. Since the masks have to be provided at pixel
level, building such a dataset for any new domain can be very time-consuming.
We present ReDO, a new model able to extract objects from images without any
annotation in an unsupervised way. It relies on the idea that it should be
possible to change the textures or colors of the objects without changing the
overall distribution of the dataset. Following this assumption, our approach is
based on an adversarial architecture where the generator is guided by an input
sample: given an image, it extracts the object mask, then redraws a new object
at the same location. The generator is controlled by a discriminator that
ensures that the distribution of generated images is aligned to the original
one. We experiment with this method on different datasets and demonstrate the
good quality of extracted masks.

negative:
The ability of the Generative Adversarial Networks (GANs) framework to learn
generative models mapping from simple latent distributions to arbitrarily
complex data distributions has been demonstrated empirically, with compelling
results showing that the latent space of such generators captures semantic
variation in the data distribution. Intuitively, models trained to predict
these semantic latent representations given data may serve as useful feature
representations for auxiliary problems where semantics are relevant. However,
in their existing form, GANs have no means of learning the inverse mapping --
projecting data back into the latent space. We propose Bidirectional Generative
Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and
demonstrate that the resulting learned feature representation is useful for
auxiliary supervised discrimination tasks, competitive with contemporary
approaches to unsupervised and self-supervised feature learning.

negative:
Unconditional image generation has recently been dominated by generative
adversarial networks (GANs). GAN methods train a generator which regresses
images from random noise vectors, as well as a discriminator that attempts to
differentiate between the generated images and a training set of real images.
GANs have shown amazing results at generating realistic looking images. Despite
their success, GANs suffer from critical drawbacks including: unstable training
and mode-dropping. The weaknesses in GANs have motivated research into
alternatives including: variational auto-encoders (VAEs), latent embedding
learning methods (e.g. GLO) and nearest-neighbor based implicit maximum
likelihood estimation (IMLE). Unfortunately at the moment, GANs still
significantly outperform the alternative methods for image generation. In this
work, we present a novel method - Generative Latent Nearest Neighbors (GLANN) -
for training generative models without adversarial training. GLANN combines the
strengths of IMLE and GLO in a way that overcomes the main drawbacks of each
method. Consequently, GLANN generates images that are far better than GLO and
IMLE. Our method does not suffer from mode collapse which plagues GAN training
and is much more stable. Qualitative results show that GLANN outperforms a
baseline consisting of 800 GANs and VAEs on commonly used datasets. Our models
are also shown to be effective for training truly non-adversarial unsupervised
image translation.

negative:
Training real-world neural network models to achieve high performance and
generalizability typically requires a substantial amount of labeled data,
spanning a broad range of variation. This data-labeling process can be both
labor and cost intensive. To achieve desirable predictive performance, a
trained model is typically applied into a domain where the data distribution is
similar to the training dataset. However, for many agricultural machine
learning problems, training datasets are collected at a specific location,
during a specific period in time of the growing season. Since agricultural
systems exhibit substantial variability in terms of crop type, cultivar,
management, seasonal growth dynamics, lighting condition, sensor type, etc, a
model trained from one dataset often does not generalize well across domains.
To enable more data efficient and generalizable neural network models in
agriculture, we propose a method that generates photorealistic agricultural
images from a synthetic 3D crop model domain into real world crop domains. The
method uses a semantically constrained GAN (generative adversarial network) to
preserve the fruit position and geometry. We observe that a baseline CycleGAN
method generates visually realistic target domain images but does not preserve
fruit position information while our method maintains fruit positions well.
Image generation results in vineyard grape day and night images show the visual
outputs of our network are much better compared to a baseline network.
Incremental training experiments in vineyard grape detection tasks show that
the images generated from our method can significantly speed the domain
adaption process, increase performance for a given number of labeled images
(i.e. data efficiency), and decrease labeling requirements.

negative:
We present a new method to create spatial data using a generative adversarial
network (GAN). Our contribution uses coarse and widely available geospatial
data to create maps of less available features at the finer scale in the built
environment, bypassing their traditional acquisition techniques (e.g. satellite
imagery or land surveying). In the work, we employ land use data and road
networks as input to generate building footprints and conduct experiments in 9
cities around the world. The method, which we implement in a tool we release
openly, enables the translation of one geospatial dataset to another with high
fidelity and morphological accuracy. It may be especially useful in locations
missing detailed and high-resolution data and those that are mapped with
uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality
of the results is influenced by the urban form and scale. In most cases, the
experiments suggest promising performance as the method tends to truthfully
indicate the locations, amount, and shape of buildings. The work has the
potential to support several applications, such as energy, climate, and urban
morphology studies in areas previously lacking required data or inpainting
geospatial data in regions with incomplete data.

negative:
Convolutional neural network (CNN) have proven its success for semantic
segmentation, which is a core task of emerging industrial applications such as
autonomous driving. However, most progress in semantic segmentation of urban
scenes is reported on standard scenarios, i.e., daytime scenes with favorable
illumination conditions. In practical applications, the outdoor weather and
illumination are changeable, e.g., cloudy and nighttime, which results in a
significant drop of semantic segmentation accuracy of CNN only trained with
daytime data. In this paper, we propose a novel generative adversarial network
(namely Mutual-GAN) to alleviate the accuracy decline when daytime-trained
neural network is applied to videos captured under adverse weather conditions.
The proposed Mutual-GAN adopts mutual information constraint to preserve
image-objects during cross-weather adaptation, which is an unsolved problem for
most unsupervised image-to-image translation approaches (e.g., CycleGAN). The
proposed Mutual-GAN is evaluated on two publicly available driving video
datasets (i.e., CamVid and SYNTHIA). The experimental results demonstrate that
our Mutual-GAN can yield visually plausible translated images and significantly
improve the semantic segmentation accuracy of daytime-trained deep learning
network while processing videos under challenging weathers.

negative:
We propose the novel use of a generative adversarial network (GAN) (i) to
make predictions in time (PredGAN) and (ii) to assimilate measurements
(DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like
properties of generative models and the ability to simulate forwards and
backwards in time. GANs have received much attention recently, after achieving
excellent results for their generation of realistic-looking images. We wish to
explore how this property translates to new applications in computational
modelling and to exploit the adjoint-like properties for efficient data
assimilation. To predict the spread of COVID-19 in an idealised town, we apply
these methods to a compartmental model in epidemiology that is able to model
space and time variations. To do this, the GAN is set within a reduced-order
model (ROM), which uses a low-dimensional space for the spatial distribution of
the simulation states. Then the GAN learns the evolution of the low-dimensional
states over time. The results show that the proposed methods can accurately
predict the evolution of the high-fidelity numerical simulation, and can
efficiently assimilate observed data and determine the corresponding model
parameters.

negative:
The adverse haze weather condition has brought considerable difficulties in
vision-based environmental applications. While, until now, most of the existing
environmental monitoring studies are under ordinary conditions, and the studies
of complex haze weather conditions have been ignored. Thence, this paper
proposes a feature-supervised learning network based on generative adversarial
networks (GAN) for environmental monitoring during hazy days. Its main idea is
to train the model under the supervision of feature maps from the ground truth.
Four key technical contributions are made in the paper. First, pairs of hazy
and clean images are used as inputs to supervise the encoding process and
obtain high-quality feature maps. Second, the basic GAN formulation is modified
by introducing perception loss, style loss, and feature regularization loss to
generate better results. Third, multi-scale images are applied as the input to
enhance the performance of discriminator. Finally, a hazy remote sensing
dataset is created for testing our dehazing method and environmental detection.
Extensive experimental results show that the proposed method has achieved
better performance than current state-of-the-art methods on both synthetic
datasets and real-world remote sensing images.

negative:
An open secret in contemporary machine learning is that many models work
beautifully on standard benchmarks but fail to generalize outside the lab. This
has been attributed to biased training data, which provide poor coverage over
real world events. Generative models are no exception, but recent advances in
generative adversarial networks (GANs) suggest otherwise - these models can now
synthesize strikingly realistic and diverse images. Is generative modeling of
photos a solved problem? We show that although current GANs can fit standard
datasets very well, they still fall short of being comprehensive models of the
visual manifold. In particular, we study their ability to fit simple
transformations such as camera movements and color changes. We find that the
models reflect the biases of the datasets on which they are trained (e.g.,
centered objects), but that they also exhibit some capacity for generalization:
by "steering" in latent space, we can shift the distribution while still
creating realistic images. We hypothesize that the degree of distributional
shift is related to the breadth of the training data distribution. Thus, we
conduct experiments to quantify the limits of GAN transformations and introduce
techniques to mitigate the problem. Code is released on our project page:
https://ali-design.github.io/gan_steerability/

negative:
Training robust supervised deep learning models for many geospatial
applications of computer vision is difficult due to dearth of class-balanced
and diverse training data. Conversely, obtaining enough training data for many
applications is financially prohibitive or may be infeasible, especially when
the application involves modeling rare or extreme events. Synthetically
generating data (and labels) using a generative model that can sample from a
target distribution and exploit the multi-scale nature of images can be an
inexpensive solution to address scarcity of labeled data. Towards this goal, we
present a deep conditional generative model, called VAE-Info-cGAN, that
combines a Variational Autoencoder (VAE) with a conditional Information
Maximizing Generative Adversarial Network (InfoGAN), for synthesizing
semantically rich images simultaneously conditioned on a pixel-level condition
(PLC) and a macroscopic feature-level condition (FLC). Dimensionally, the PLC
can only vary in the channel dimension from the synthesized image and is meant
to be a task-specific input. The FLC is modeled as an attribute vector in the
latent space of the generated image which controls the contributions of various
characteristic attributes germane to the target distribution. Experiments on a
GPS trajectories dataset show that the proposed model can accurately generate
various forms of spatiotemporal aggregates across different geographic
locations while conditioned only on a raster representation of the road
network. The primary intended application of the VAE-Info-cGAN is synthetic
data (and label) generation for targeted data augmentation for computer
vision-based modeling of problems relevant to geospatial analysis and remote
sensing.

negative:
The SARS-CoV-2 (Covid-19) pandemic has caused significant strain on public
health institutions around the world. Contact tracing is an essential tool to
change the course of the Covid-19 pandemic. Manual contact tracing of Covid-19
cases has significant challenges that limit the ability of public health
authorities to minimize community infections. Personalized peer-to-peer contact
tracing through the use of mobile apps has the potential to shift the paradigm.
Some countries have deployed centralized tracking systems, but more
privacy-protecting decentralized systems offer much of the same benefit without
concentrating data in the hands of a state authority or for-profit
corporations. Machine learning methods can circumvent some of the limitations
of standard digital tracing by incorporating many clues and their uncertainty
into a more graded and precise estimation of infection risk. The estimated risk
can provide early risk awareness, personalized recommendations and relevant
information to the user. Finally, non-identifying risk data can inform
epidemiological models trained jointly with the machine learning predictor.
These models can provide statistical evidence for the importance of factors
involved in disease transmission. They can also be used to monitor, evaluate
and optimize health policy and (de)confinement scenarios according to medical
and economic productivity indicators. However, such a strategy based on mobile
apps and machine learning should proactively mitigate potential ethical and
privacy risks, which could have substantial impacts on society (not only
impacts on health but also impacts such as stigmatization and abuse of personal
data). Here, we present an overview of the rationale, design, ethical
considerations and privacy strategy of `COVI,' a Covid-19 public peer-to-peer
contact tracing and risk awareness mobile application developed in Canada.

negative:
Internal learning for single-image generation is a framework, where a
generator is trained to produce novel images based on a single image. Since
these models are trained on a single image, they are limited in their scale and
application. To overcome these issues, we propose a meta-learning approach that
enables training over a collection of images, in order to model the internal
statistics of the sample image more effectively. In the presented meta-learning
approach, a single-image GAN model is generated given an input image, via a
convolutional feedforward hypernetwork $f$. This network is trained over a
dataset of images, allowing for feature sharing among different models, and for
interpolation in the space of generative models. The generated single-image
model contains a hierarchy of multiple generators and discriminators. It is
therefore required to train the meta-learner in an adversarial manner, which
requires careful design choices that we justify by a theoretical analysis. Our
results show that the models obtained are as suitable as single-image GANs for
many common image applications, significantly reduce the training time per
image without loss in performance, and introduce novel capabilities, such as
interpolation and feedforward modeling of novel images.

negative:
One of the major prerequisites for any deep learning approach is the
availability of large-scale training data. When dealing with scanned document
images in real world scenarios, the principal information of its content is
stored in the layout itself. In this work, we have proposed an automated deep
generative model using Graph Neural Networks (GNNs) to generate synthetic data
with highly variable and plausible document layouts that can be used to train
document interpretation systems, in this case, specially in digital mailroom
applications. It is also the first graph-based approach for document layout
generation task experimented on administrative document images, in this case,
invoices.

negative:
Document classification is a challenging task with important applications.
The deep learning approaches to the problem have gained much attention
recently. Despite the progress, the proposed models do not incorporate the
knowledge of the document structure in the architecture efficiently and not
take into account the contexting importance of words and sentences. In this
paper, we propose a new approach based on a combination of convolutional neural
networks, gated recurrent units, and attention mechanisms for document
classification tasks. The main contribution of this work is the use of
convolution layers to extract more meaningful, generalizable and abstract
features by the hierarchical representation. The proposed method in this paper
improves the results of the current attention-based approaches for document
classification.

negative:
A lot of research has been devoted to identity documents analysis and
recognition on mobile devices. However, no publicly available datasets designed
for this particular problem currently exist. There are a few datasets which are
useful for associated subtasks but in order to facilitate a more comprehensive
scientific and technical approach to identity document recognition more
specialized datasets are required. In this paper we present a Mobile Identity
Document Video dataset (MIDV-500) consisting of 500 video clips for 50
different identity document types with ground truth which allows to perform
research in a wide scope of document analysis problems. The paper presents
characteristics of the dataset and evaluation results for existing methods of
face detection, text line recognition, and document fields data extraction.
Since an important feature of identity documents is their sensitiveness as they
contain personal data, all source document images used in MIDV-500 are either
in public domain or distributed under public copyright licenses.
  The main goal of this paper is to present a dataset. However, in addition and
as a baseline, we present evaluation results for existing methods for face
detection, text line recognition, and document data extraction, using the
presented dataset.
  (The dataset is available for download at ftp://smartengines.com/midv-500/.)

negative:
Additive Manufacturing (AM) is a crucial component of the smart industry. In
this paper, we propose an automated quality grading system for the AM process
using a deep convolutional neural network (CNN) model. The CNN model is trained
offline using the images of the internal and surface defects in the
layer-by-layer deposition of materials and tested online by studying the
performance of detecting and classifying the failure in AM process at different
extruder speeds and temperatures. The model demonstrates the accuracy of 94%
and specificity of 96%, as well as above 75% in three classifier measures of
the Fscore, the sensitivity, and precision for classifying the quality of the
printing process in five grades in real-time. The proposed online model adds an
automated, consistent, and non-contact quality control signal to the AM process
that eliminates the manual inspection of parts after they are entirely built.
The quality monitoring signal can also be used by the machine to suggest
remedial actions by adjusting the parameters in real-time. The proposed quality
predictive model serves as a proof-of-concept for any type of AM machines to
produce reliable parts with fewer quality hiccups while limiting the waste of
both time and materials.

negative:
With the advent of mobile and hand-held cameras, document images have found
their way into almost every domain. Dewarping of these images for the removal
of perspective distortions and folds is essential so that they can be
understood by document recognition algorithms. For this, we propose an
end-to-end CNN architecture that can produce distortion free document images
from warped documents it takes as input. We train this model on warped document
images simulated synthetically to compensate for lack of enough natural data.
Our method is novel in the use of a bifurcated decoder with shared weights to
prevent intermingling of grid coordinates, in the use of residual networks in
the U-Net skip connections to allow flow of data from different receptive
fields in the model, and in the use of a gated network to help the model focus
on structure and line level detail of the document image. We evaluate our
method on the DocUNet dataset, a benchmark in this domain, and obtain results
comparable to state-of-the-art methods.

negative:
Creating fake images and videos such as "Deepfake" has become much easier
these days due to the advancement in Generative Adversarial Networks (GANs).
Moreover, recent research such as the few-shot learning can create highly
realistic personalized fake images with only a few images. Therefore, the
threat of Deepfake to be used for a variety of malicious intents such as
propagating fake images and videos becomes prevalent. And detecting these
machine-generated fake images has been quite challenging than ever. In this
work, we propose a light-weight robust fine-tuning neural network-based
classifier architecture called Fake Detection Fine-tuning Network (FDFtNet),
which is capable of detecting many of the new fake face image generation
models, and can be easily combined with existing image classification networks
and finetuned on a few datasets. In contrast to many existing methods, our
approach aims to reuse popular pre-trained models with only a few images for
fine-tuning to effectively detect fake images. The core of our approach is to
introduce an image-based self-attention module called Fine-Tune Transformer
that uses only the attention module and the down-sampling layer. This module is
added to the pre-trained model and fine-tuned on a few data to search for new
sets of feature space to detect fake images. We experiment with our FDFtNet on
the GANsbased dataset (Progressive Growing GAN) and Deepfake-based dataset
(Deepfake and Face2Face) with a small input image resolution of 64x64 that
complicates detection. Our FDFtNet achieves an overall accuracy of 90.29% in
detecting fake images generated from the GANs-based dataset, outperforming the
state-of-the-art.

negative:
Climate change is one of the greatest challenges facing humanity, and we, as
machine learning experts, may wonder how we can help. Here we describe how
machine learning can be a powerful tool in reducing greenhouse gas emissions
and helping society adapt to a changing climate. From smart grids to disaster
management, we identify high impact problems where existing gaps can be filled
by machine learning, in collaboration with other fields. Our recommendations
encompass exciting research questions as well as promising business
opportunities. We call on the machine learning community to join the global
effort against climate change.

negative:
As AI-based medical devices are becoming more common in imaging fields like
radiology and histology, interpretability of the underlying predictive models
is crucial to expand their use in clinical practice. Existing heatmap-based
interpretability methods such as GradCAM only highlight the location of
predictive features but do not explain how they contribute to the prediction.
In this paper, we propose a new interpretability method that can be used to
understand the predictions of any black-box model on images, by showing how the
input image would be modified in order to produce different predictions. A
StyleGAN is trained on medical images to provide a mapping between latent
vectors and images. Our method identifies the optimal direction in the latent
space to create a change in the model prediction. By shifting the latent
representation of an input image along this direction, we can produce a series
of new synthetic images with changed predictions. We validate our approach on
histology and radiology images, and demonstrate its ability to provide
meaningful explanations that are more informative than GradCAM heatmaps. Our
method reveals the patterns learned by the model, which allows clinicians to
build trust in the model's predictions, discover new biomarkers and eventually
reveal potential biases.

negative:
Taking a photo outside, can we predict the immediate future, e.g., how would
the cloud move in the sky? We address this problem by presenting a generative
adversarial network (GAN) based two-stage approach to generating realistic
time-lapse videos of high resolution. Given the first frame, our model learns
to generate long-term future frames. The first stage generates videos of
realistic contents for each frame. The second stage refines the generated video
from the first stage by enforcing it to be closer to real videos with regard to
motion dynamics. To further encourage vivid motion in the final generated
video, Gram matrix is employed to model the motion more precisely. We build a
large scale time-lapse dataset, and test our approach on this new dataset.
Using our model, we are able to generate realistic videos of up to $128\times
128$ resolution for 32 frames. Quantitative and qualitative experiment results
have demonstrated the superiority of our model over the state-of-the-art
models.

negative:
Training robust supervised deep learning models for many geospatial
applications of computer vision is difficult due to dearth of class-balanced
and diverse training data. Conversely, obtaining enough training data for many
applications is financially prohibitive or may be infeasible, especially when
the application involves modeling rare or extreme events. Synthetically
generating data (and labels) using a generative model that can sample from a
target distribution and exploit the multi-scale nature of images can be an
inexpensive solution to address scarcity of labeled data. Towards this goal, we
present a deep conditional generative model, called VAE-Info-cGAN, that
combines a Variational Autoencoder (VAE) with a conditional Information
Maximizing Generative Adversarial Network (InfoGAN), for synthesizing
semantically rich images simultaneously conditioned on a pixel-level condition
(PLC) and a macroscopic feature-level condition (FLC). Dimensionally, the PLC
can only vary in the channel dimension from the synthesized image and is meant
to be a task-specific input. The FLC is modeled as an attribute vector in the
latent space of the generated image which controls the contributions of various
characteristic attributes germane to the target distribution. An interpretation
of the attribute vector to systematically generate synthetic images by varying
a chosen binary macroscopic feature is explored. Experiments on a GPS
trajectories dataset show that the proposed model can accurately generate
various forms of spatio-temporal aggregates across different geographic
locations while conditioned only on a raster representation of the road
network. The primary intended application of the VAE-Info-cGAN is synthetic
data (and label) generation for targeted data augmentation for computer
vision-based modeling of problems relevant to geospatial analysis and remote
sensing.

negative:
This paper presents results of Document Visual Question Answering Challenge
organized as part of "Text and Documents in the Deep Learning Era" workshop, in
CVPR 2020. The challenge introduces a new problem - Visual Question Answering
on document images. The challenge comprised two tasks. The first task concerns
with asking questions on a single document image. On the other hand, the second
task is set as a retrieval task where the question is posed over a collection
of images. For the task 1 a new dataset is introduced comprising 50,000
questions-answer(s) pairs defined over 12,767 document images. For task 2
another dataset has been created comprising 20 questions over 14,362 document
images which share the same document template.

negative:
Identity of a vehicle is done through the vehicle license plate by traffic
police in general. Au- tomatic vehicle license plate recognition has several
applications in intelligent traffic management systems. The security situation
across the globe and particularly in India demands a need to equip the traffic
police with a system that enables them to get instant details of a vehicle. The
system should be easy to use, should be mobile, and work 24 x 7. In this paper,
we describe a mobile phone based, client-server architected, license plate
recognition system. While we use the state of the art image processing and
pattern recognition algorithms tuned for Indian conditions to automatically
recognize non-uniform license plates, the main contribution is in creating an
end to end usable solution. The client application runs on a mobile device and
a server application, with access to vehicle information database, is hosted
centrally. The solution enables capture of license plate image captured by the
phone camera and passes to the server; on the server the license plate number
is recognized; the data associated with the number plate is then sent back to
the mobile device, instantaneously. We describe the end to end system
architecture in detail. A working prototype of the proposed system has been
implemented in the lab environment.

negative:
This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive representations. The current work explores this capacity in the
realm of document analysis, and confirms that this representation strategy is
superior to a variety of popular hand-crafted alternatives. Experiments also
show that (i) features extracted from CNNs are robust to compression, (ii) CNNs
trained on non-document images transfer well to document analysis tasks, and
(iii) enforcing region-specific feature-learning is unnecessary given
sufficient training data. This work also makes available a new labelled subset
of the IIT-CDIP collection, containing 400,000 document images across 16
categories, useful for training new CNNs for document analysis.

negative:
Document-level relation extraction aims to extract relations among entities
within a document. Different from sentence-level relation extraction, it
requires reasoning over multiple sentences across a document. In this paper, we
propose Graph Aggregation-and-Inference Network (GAIN) featuring double graphs.
GAIN first constructs a heterogeneous mention-level graph (hMG) to model
complex interaction among different mentions across the document. It also
constructs an entity-level graph (EG), based on which we propose a novel path
reasoning mechanism to infer relations between entities. Experiments on the
public dataset, DocRED, show GAIN achieves a significant performance
improvement (2.85 on F1) over the previous state-of-the-art. Our code is
available at https://github.com/DreamInvoker/GAIN .

negative:
The quality of a document is affected by various factors, including
grammaticality, readability, stylistics, and expertise depth, making the task
of document quality assessment a complex one. In this paper, we explore this
task in the context of assessing the quality of Wikipedia articles and academic
papers. Observing that the visual rendering of a document can capture implicit
quality indicators that are not present in the document text --- such as
images, font choices, and visual layout --- we propose a joint model that
combines the text content with a visual rendering of the document for document
quality assessment. Experimental results over two datasets reveal that textual
and visual features are complementary, achieving state-of-the-art results.

negative:
In this work, a region-based Deep Convolutional Neural Network framework is
proposed for document structure learning. The contribution of this work
involves efficient training of region based classifiers and effective
ensembling for document image classification. A primary level of `inter-domain'
transfer learning is used by exporting weights from a pre-trained VGG16
architecture on the ImageNet dataset to train a document classifier on whole
document images. Exploiting the nature of region based influence modelling, a
secondary level of `intra-domain' transfer learning is used for rapid training
of deep learning models for image segments. Finally, stacked generalization
based ensembling is utilized for combining the predictions of the base deep
neural network models. The proposed method achieves state-of-the-art accuracy
of 92.2% on the popular RVL-CDIP document image dataset, exceeding benchmarks
set by existing algorithms.

negative:
The digital camera captured document images may often be warped and distorted
due to different camera angles or document surfaces. A robust technique is
needed to solve this kind of distortion. The research on dewarping of the
document suffers due to the limited availability of benchmark public dataset.
In recent times, deep learning based approaches are used to solve the problems
accurately. To train most of the deep neural networks a large number of
document images is required and generating such a large volume of document
images manually is difficult. In this paper, we propose a technique to generate
a synthetic warped image from a flat-bedded scanned document image. It is done
by calculating warping factors for each pixel position using two warping
position parameters (WPP) and eight warping control parameters (WCP). These
parameters can be specified as needed depending upon the desired warping. The
results are compared with similar real captured images both qualitative and
quantitative way.

negative:
Rapid increase of digitized document give birth to high demand of document
image retrieval. While conventional document image retrieval approaches depend
on complex OCR-based text recognition and text similarity detection, this paper
proposes a new content-based approach, in which more attention is paid to
features extraction and fusion. In the proposed approach, multiple features of
document images are extracted by different CNN models. After that, the
extracted CNN features are reduced and fused into weighted average feature.
Finally, the document images are ranked based on feature similarity to a
provided query image. Experimental procedure is performed on a group of
document images that transformed from academic papers, which contain both
English and Chinese document, the results show that the proposed approach has
good ability to retrieve document images with similar text content, and the
fusion of CNN features can effectively improve the retrieval accuracy.

negative:
Context-aware neural machine translation (NMT) is a promising direction to
improve the translation quality by making use of the additional context, e.g.,
document-level translation, or having meta-information. Although there exist
various architectures and analyses, the effectiveness of different
context-aware NMT models is not well explored yet. This paper analyzes the
performance of document-level NMT models on four diverse domains with a varied
amount of parallel document-level bilingual data. We conduct a comprehensive
set of experiments to investigate the impact of document-level NMT. We find
that there is no single best approach to document-level NMT, but rather that
different architectures come out on top on different tasks. Looking at
task-specific problems, such as pronoun resolution or headline translation, we
find improvements in the context-aware systems, even in cases where the
corpus-level metrics like BLEU show no significant improvement. We also show
that document-level back-translation significantly helps to compensate for the
lack of document-level bi-texts.

negative:
The first phase of table recognition is to detect the tabular area in a
document. Subsequently, the tabular structures are recognized in the second
phase in order to extract information from the respective cells. Table
detection and structural recognition are pivotal problems in the domain of
table understanding. However, table analysis is a perplexing task due to the
colossal amount of diversity and asymmetry in tables. Therefore, it is an
active area of research in document image analysis. Recent advances in the
computing capabilities of graphical processing units have enabled deep neural
networks to outperform traditional state-of-the-art machine learning methods.
Table understanding has substantially benefited from the recent breakthroughs
in deep neural networks. However, there has not been a consolidated description
of the deep learning methods for table detection and table structure
recognition. This review paper provides a thorough analysis of the modern
methodologies that utilize deep neural networks. This work provided a thorough
understanding of the current state-of-the-art and related challenges of table
understanding in document images. Furthermore, the leading datasets and their
intricacies have been elaborated along with the quantitative results. Moreover,
a brief overview is given regarding the promising directions that can serve as
a guide to further improve table analysis in document images.

negative:
Cross-lingual document representations enable language understanding in
multilingual contexts and allow transfer learning from high-resource to
low-resource languages at the document level. Recently large pre-trained
language models such as BERT, XLM and XLM-RoBERTa have achieved great success
when fine-tuned on sentence-level downstream tasks. It is tempting to apply
these cross-lingual models to document representation learning. However, there
are two challenges: (1) these models impose high costs on long document
processing and thus many of them have strict length limit; (2) model
fine-tuning requires extra data and computational resources, which is not
practical in resource-limited settings. In this work, we address these
challenges by proposing unsupervised Language-Agnostic Weighted Document
Representations (LAWDR). We study the geometry of pre-trained sentence
embeddings and leverage it to derive document representations without
fine-tuning. Evaluated on cross-lingual document alignment, LAWDR demonstrates
comparable performance to state-of-the-art models on benchmark datasets.

negative:
Document Layout Analysis is a fundamental step in Handwritten Text Processing
systems, from the extraction of the text lines to the type of zone it belongs
to. We present a system based on artificial neural networks which is able to
determine not only the baselines of text lines present in the document, but
also performs geometric and logic layout analysis of the document. Experiments
in three different datasets demonstrate the potential of the method and show
competitive results with respect to state-of-the-art methods.

negative:
Graphical elements: particularly tables and figures contain a visual summary
of the most valuable information contained in a document. Therefore,
localization of such graphical objects in the document images is the initial
step to understand the content of such graphical objects or document images. In
this paper, we present a novel end-to-end trainable deep learning based
framework to localize graphical objects in the document images called as
Graphical Object Detection (GOD). Our framework is data-driven and does not
require any heuristics or meta-data to locate graphical objects in the document
images. The GOD explores the concept of transfer learning and domain adaptation
to handle scarcity of labeled training images for graphical object detection
task in the document images. Performance analysis carried out on the various
public benchmark data sets: ICDAR-2013, ICDAR-POD2017,and UNLV shows that our
model yields promising results as compared to state-of-the-art techniques.

negative:
When digitizing a document into an image, it is common to include a
surrounding border region to visually indicate that the entire document is
present in the image. However, this border should be removed prior to automated
processing. In this work, we present a deep learning based system, PageNet,
which identifies the main page region in an image in order to segment content
from both textual and non-textual border noise. In PageNet, a Fully
Convolutional Network obtains a pixel-wise segmentation which is post-processed
into the output quadrilateral region. We evaluate PageNet on 4 collections of
historical handwritten documents and obtain over 94% mean intersection over
union on all datasets and approach human performance on 2 of these collections.
Additionally, we show that PageNet can segment documents that are overlayed on
top of other documents.

negative:
Identity documents recognition is an important sub-field of document
analysis, which deals with tasks of robust document detection, type
identification, text fields recognition, as well as identity fraud prevention
and document authenticity validation given photos, scans, or video frames of an
identity document capture. Significant amount of research has been published on
this topic in recent years, however a chief difficulty for such research is
scarcity of datasets, due to the subject matter being protected by security
requirements. A few datasets of identity documents which are available lack
diversity of document types, capturing conditions, or variability of document
field values. In addition, the published datasets were typically designed only
for a subset of document recognition problems, not for a complex identity
document analysis. In this paper, we present a dataset MIDV-2020 which consists
of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock
identity documents, each with unique text field values and unique artificially
generated faces, with rich annotation. For the presented benchmark dataset
baselines are provided for such tasks as document location and identification,
text fields recognition, and face detection. With 72409 annotated images in
total, to the date of publication the proposed dataset is the largest publicly
available identity documents dataset with variable artificially generated data,
and we believe that it will prove invaluable for advancement of the field of
document analysis and recognition. The dataset is available for download at
ftp://smartengines.com/midv-2020 and http://l3i-share.univ-lr.fr .

negative:
In this paper, we focus on the problem of unsupervised image-sentence
matching. Existing research explores to utilize document-level structural
information to sample positive and negative instances for model training.
Although the approach achieves positive results, it introduces a sampling bias
and fails to distinguish instances with high semantic similarity. To alleviate
the bias, we propose a new sampling strategy to select additional
intra-document image-sentence pairs as positive or negative samples.
Furthermore, to recognize the complex pattern in intra-document samples, we
propose a Transformer based model to capture fine-grained features and
implicitly construct a graph for each document, where concepts in a document
are introduced to bridge the representation learning of images and sentences in
the context of a document. Experimental results show the effectiveness of our
approach to alleviate the bias and learn well-aligned multimodal
representations.

negative:
It has been shown that most machine learning algorithms are susceptible to
adversarial perturbations. Slightly perturbing an image in a carefully chosen
direction in the image space may cause a trained neural network model to
misclassify it. Recently, it was shown that physical adversarial examples
exist: printing perturbed images then taking pictures of them would still
result in misclassification. This raises security and safety concerns.
  However, these experiments ignore a crucial property of physical objects: the
camera can view objects from different distances and at different angles. In
this paper, we show experiments that suggest that current constructions of
physical adversarial examples do not disrupt object detection from a moving
platform. Instead, a trained neural network classifies most of the pictures
taken from different distances and angles of a perturbed image correctly. We
believe this is because the adversarial property of the perturbation is
sensitive to the scale at which the perturbed picture is viewed, so (for
example) an autonomous car will misclassify a stop sign only from a small range
of distances.
  Our work raises an important question: can one construct examples that are
adversarial for many or most viewing conditions? If so, the construction should
offer very significant insights into the internal representation of patterns by
deep networks. If not, there is a good prospect that adversarial examples can
be reduced to a curiosity with little practical impact.

negative:
Document-level relation extraction aims to discover relations between
entities across a whole document. How to build the dependency of entities from
different sentences in a document remains to be a great challenge. Current
approaches either leverage syntactic trees to construct document-level graphs
or aggregate inference information from different sentences. In this paper, we
build cross-sentence dependencies by inferring compositional relations between
inter-sentence mentions. Adopting aggressive linking strategy, intermediate
relations are reasoned on the document-level graphs by mention convolution. We
further notice the generalization problem of NA instances, which is caused by
incomplete annotation and worsened by fully-connected mention pairs. An
improved ranking loss is proposed to attend this problem. Experiments show the
connections between different mentions are crucial to document-level relation
extraction, which enables the model to extract more meaningful higher-level
compositional relations.

negative:
In this paper we investigate forgetting in disjunctive logic programs, where
forgetting an atom from a program amounts to a reduction in the signature of
that program. The goal is to provide an approach that is syntax-independent, in
that if two programs are strongly equivalent, then the results of forgetting an
atom in each program should also be strongly equivalent. Our central definition
of forgetting is impractical but satisfies this goal: Forgetting an atom is
characterised by the set of SE consequences of the program that do not mention
the atom to be forgotten. We then provide an equivalent, practical definition,
wherein forgetting an atom $p$ is given by those rules in the program that
don't mention $p$, together with rules obtained by a single inference step from
rules that do mention $p$. Forgetting is shown to have appropriate properties;
as well, the finite characterisation results in a modest (at worst quadratic)
blowup. Finally we have also obtained a prototype implementation of this
approach to forgetting.

negative:
This paper describes an application of reinforcement learning to the mention
detection task. We define a novel action-based formulation for the mention
detection task, in which a model can flexibly revise past labeling decisions by
grouping together tokens and assigning partial mention labels. We devise a
method to create mention-level episodes and we train a model by rewarding
correctly labeled complete mentions, irrespective of the inner structure
created. The model yields results which are on par with a competitive
supervised counterpart while being more flexible in terms of achieving targeted
behavior through reward modeling and generating internal mention structure,
especially on longer mentions.

negative:
One major drawback of state of the art Neural Networks (NN)-based approaches
for document classification purposes is the large number of training samples
required to obtain an efficient classification. The minimum required number is
around one thousand annotated documents for each class. In many cases it is
very difficult, if not impossible, to gather this number of samples in real
industrial processes. In this paper, we analyse the efficiency of NN-based
document classification systems in a sub-optimal training case, based on the
situation of a company document stream. We evaluated three different
approaches, one based on image content and two on textual content. The
evaluation was divided into four parts: a reference case, to assess the
performance of the system in the lab; two cases that each simulate a specific
difficulty linked to document stream processing; and a realistic case that
combined all of these difficulties. The realistic case highlighted the fact
that there is a significant drop in the efficiency of NN-Based document
classification systems. Although they remain efficient for well represented
classes (with an over-fitting of the system for those classes), it is
impossible for them to handle appropriately less well represented classes.
NN-Based document classification systems need to be adapted to resolve these
two problems before they can be considered for use in a company document
stream.

negative:
Named entity linking is to map an ambiguous mention in documents to an entity
in a knowledge base. The named entity linking is challenging, given the fact
that there are multiple candidate entities for a mention in a document. It is
difficult to link a mention when it appears multiple times in a document, since
there are conflicts by the contexts around the appearances of the mention. In
addition, it is difficult since the given training dataset is small due to the
reason that it is done manually to link a mention to its mapping entity. In the
literature, there are many reported studies among which the recent embedding
methods learn vectors of entities from the training dataset at document level.
To address these issues, we focus on how to link entity for mentions at a
sentence level, which reduces the noises introduced by different appearances of
the same mention in a document at the expense of insufficient information to be
used. We propose a new unified embedding method by maximizing the relationships
learned from knowledge graphs. We confirm the effectiveness of our method in
our experimental studies.

negative:
Modern entity linking systems rely on large collections of documents
specifically annotated for the task (e.g., AIDA CoNLL). In contrast, we propose
an approach which exploits only naturally occurring information: unlabeled
documents and Wikipedia. Our approach consists of two stages. First, we
construct a high recall list of candidate entities for each mention in an
unlabeled document. Second, we use the candidate lists as weak supervision to
constrain our document-level entity linking model. The model treats entities as
latent variables and, when estimated on a collection of unlabelled texts,
learns to choose entities relying both on local context of each mention and on
coherence with other entities in the document. The resulting approach rivals
fully-supervised state-of-the-art systems on standard test sets. It also
approaches their performance in the very challenging setting: when tested on a
test set sampled from the data used to estimate the supervised systems. By
comparing to Wikipedia-only training of our model, we demonstrate that modeling
unlabeled documents is beneficial.

negative:
Various neural-based methods have been proposed so far for joint mention
detection and coreference resolution. However, existing works on coreference
resolution are mainly dependent on filtered mention representation, while other
spans are largely neglected. In this paper, we aim at increasing the
utilization rate of data and investigating whether those eliminated spans are
totally useless, or to what extent they can improve the performance of
coreference resolution. To achieve this, we propose a mention representation
refining strategy where spans highly related to mentions are well leveraged
using a pointer network for representation enhancing. Notably, we utilize an
additional loss term in this work to encourage the diversity between entity
clusters. Experimental results on the document-level CoNLL-2012 Shared Task
English dataset show that eliminated spans are indeed much effective and our
approach can achieve competitive results when compared with previous
state-of-the-art in coreference resolution.

negative:
Relation extraction (RE) aims to identify the semantic relations between
named entities in text. Recent years have witnessed it raised to the document
level, which requires complex reasoning with entities and mentions throughout
an entire document. In this paper, we propose a novel model to document-level
RE, by encoding the document information in terms of entity global and local
representations as well as context relation representations. Entity global
representations model the semantic information of all entities in the document,
entity local representations aggregate the contextual information of multiple
mentions of specific entities, and context relation representations encode the
topic information of other relations. Experimental results demonstrate that our
model achieves superior performance on two public datasets for document-level
RE. It is particularly effective in extracting relations between entities of
long distance and having multiple mentions.

negative:
Document segmentation is a method of rending the document into distinct
regions. A document is an assortment of information and a standard mode of
conveying information to others. Pursuance of data from documents involves ton
of human effort, time intense and might severely prohibit the usage of data
systems. So, automatic information pursuance from the document has become a big
issue. It is been shown that document segmentation will facilitate to beat such
problems. This paper proposes a new approach to segment and classify the
document regions as text, image, drawings and table. Document image is divided
into blocks using Run length smearing rule and features are extracted from
every blocks. Discipulus tool has been used to construct the Genetic
programming based classifier model and located 97.5% classification accuracy.

negative:
Computation of document image quality metrics often depends upon the
availability of a ground truth image corresponding to the document. This limits
the applicability of quality metrics in applications such as hyperparameter
optimization of image processing algorithms that operate on-the-fly on unseen
documents. This work proposes the use of surrogate models to learn the behavior
of a given document quality metric on existing datasets where ground truth
images are available. The trained surrogate model can later be used to predict
the metric value on previously unseen document images without requiring access
to ground truth images. The surrogate model is empirically evaluated on the
Document Image Binarization Competition (DIBCO) and the Handwritten Document
Image Binarization Competition (H-DIBCO) datasets.

negative:
Document date is essential for many important tasks, such as document
retrieval, summarization, event detection, etc. While existing approaches for
these tasks assume accurate knowledge of the document date, this is not always
available, especially for arbitrary documents from the Web. Document Dating is
a challenging problem which requires inference over the temporal structure of
the document. Prior document dating systems have largely relied on handcrafted
features while ignoring such document internal structures. In this paper, we
propose NeuralDater, a Graph Convolutional Network (GCN) based document dating
approach which jointly exploits syntactic and temporal graph structures of
document in a principled way. To the best of our knowledge, this is the first
application of deep learning for the problem of document dating. Through
extensive experiments on real-world datasets, we find that NeuralDater
significantly outperforms state-of-the-art baseline by 19% absolute (45%
relative) accuracy points.