pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Image Super-resolution: A Survey
Authors: Zhihao Wang, Jian Chen, Steven C. H. Hoi
Date: 2019-02-16 00:00:00
Tasks: Deep Learning, Image Super-Resolution, Super-Resolution, Survey

Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.
BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding
Authors: Gencer Sumbul, Marcela Charfuelan, Begüm Demir, Volker Markl
Date: 2019-02-16 00:00:00
Tasks: Multi-Label Image Classification, Scene Classification

This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive. The BigEarthNet consists of 590,326 Sentinel-2 image patches, each of which is a section of i) 120x120 pixels for 10m bands; ii) 60x60 pixels for 20m bands; and iii) 20x20 pixels for 60m bands. Unlike most of the existing archives, each image patch is annotated by multiple land-cover classes (i.e., multi-labels) that are provided from the CORINE Land Cover database of the year 2018 (CLC 2018). The BigEarthNet is significantly larger than the existing archives in remote sensing (RS) and thus is much more convenient to be used as a training source in the context of deep learning. This paper first addresses the limitations of the existing archives and then describes the properties of the BigEarthNet. Experimental results obtained in the framework of RS image scene classification problems show that a shallow Convolutional Neural Network (CNN) architecture trained on the BigEarthNet provides much higher accuracy compared to a state-of-the-art CNN model pre-trained on the ImageNet (which is a very popular large-scale benchmark archive in computer vision). The BigEarthNet opens up promising directions to advance operational RS applications and research in massive Sentinel-2 image archives.
Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
Authors: Longlong Jing, YingLi Tian
Date: 2019-02-16 00:00:00
Tasks: Self-Supervised Image Classification, Self-Supervised Learning

Large-scale labeled data are generally required to train deep neural networks
in order to obtain better performance in visual feature learning from images or
videos for computer vision applications. To avoid extensive cost of collecting
and annotating large-scale datasets, as a subset of unsupervised learning
methods, self-supervised learning methods are proposed to learn general image
and video features from large-scale unlabeled data without using any
human-annotated labels. This paper provides an extensive review of deep
learning-based self-supervised general visual feature learning methods from
images or videos. First, the motivation, general pipeline, and terminologies of
this field are described. Then the common deep neural network architectures
that used for self-supervised learning are summarized. Next, the main
components and evaluation metrics of self-supervised learning methods are
reviewed followed by the commonly used image and video datasets and the
existing self-supervised visual feature learning methods. Finally, quantitative
performance comparisons of the reviewed methods on benchmark datasets are
summarized and discussed for both image and video feature learning. At last,
this paper is concluded and lists a set of promising future directions for
self-supervised visual feature learning.
Fully-Featured Attribute Transfer
Authors: De Xie, Muli Yang, Cheng Deng, Wei Liu, DaCheng Tao
Date: 2019-02-17 00:00:00
Tasks: Attribute, Image Generation

Image attribute transfer aims to change an input image to a target one with
expected attributes, which has received significant attention in recent years.
However, most of the existing methods lack the ability to de-correlate the
target attributes and irrelevant information, i.e., the other attributes and
background information, thus often suffering from blurs and artifacts. To
address these issues, we propose a novel Attribute Manifold Encoding GAN
(AME-GAN) for fully-featured attribute transfer, which can modify and adjust
every detail in the images. Specifically, our method divides the input image
into image attribute part and image background part on manifolds, which are
controlled by attribute latent variables and background latent variables
respectively. Through enforcing attribute latent variables to Gaussian
distributions and background latent variables to uniform distributions
respectively, the attribute transfer procedure becomes controllable and image
generation is more photo-realistic. Furthermore, we adopt a conditional
multi-scale discriminator to render accurate and high-quality target attribute
images. Experimental results on three popular datasets demonstrate the
superiority of our proposed method in both performances of the attribute
transfer and image generation quality.
DIViS: Domain Invariant Visual Servoing for Collision-Free Goal Reaching
Authors: Fereshteh Sadeghi
Date: 2019-02-18 00:00:00

Robots should understand both semantics and physics to be functional in the
real world. While robot platforms provide means for interacting with the
physical world they cannot autonomously acquire object-level semantics without
needing human. In this paper, we investigate how to minimize human effort and
intervention to teach robots perform real world tasks that incorporate
semantics. We study this question in the context of visual servoing of mobile
robots and propose DIViS, a Domain Invariant policy learning approach for
collision free Visual Servoing. DIViS incorporates high level semantics from
previously collected static human-labeled datasets and learns collision free
servoing entirely in simulation and without any real robot data. However, DIViS
can directly be deployed on a real robot and is capable of servoing to the
user-specified object categories while avoiding collisions in the real world.
DIViS is not constrained to be queried by the final view of goal but rather is
robust to servo to image goals taken from initial robot view with high
occlusions without this impairing its ability to maintain a collision free
path. We show the generalization capability of DIViS on real mobile robots in
more than 90 real world test scenarios with various unseen object goals in
unstructured environments. DIViS is compared to prior approaches via real world
experiments and rigorous tests in simulation. For supplementary videos, see:
\href{https://fsadeghi.github.io/DIViS}{https://fsadeghi.github.io/DIViS}
Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks
Authors: Jinyong Hou, Xuejie Ding, Jeremiah D. Deng, Stephen Cranefield
Date: 2019-02-17 00:00:00
Tasks: Decoder, Domain Adaptation, Representation Learning, Unsupervised Domain Adaptation

Current deep domain adaptation methods used in computer vision have mainly
focused on learning discriminative and domain-invariant features across
different domains. In this paper, we present a novel approach that bridges the
domain gap by projecting the source and target domains into a common
association space through an unsupervised ``cross-grafted representation
stacking'' (CGRS) mechanism. Specifically, we construct variational
auto-encoders (VAE) for the two domains, and form bidirectional associations by
cross-grafting the VAEs' decoder stacks. Furthermore, generative adversarial
networks (GAN) are employed for label alignment (LA), mapping the target domain
data to the known label space of the source domain. The overall adaptation
process hence consists of three phases: feature representation learning by
VAEs, association generation, and association label alignment by GANs.
Experimental results demonstrate that our CGRS-LA approach outperforms the
state-of-the-art on a number of unsupervised domain adaptation benchmarks.
Collaborative Similarity Embedding for Recommender Systems
Authors: Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yi-Hsuan Yang
Date: 2019-02-17 00:00:00
Tasks: Graph Learning, Recommendation Systems, Representation Learning

We present collaborative similarity embedding (CSE), a unified framework that
exploits comprehensive collaborative relations available in a user-item
bipartite graph for representation learning and recommendation. In the proposed
framework, we differentiate two types of proximity relations: direct proximity
and k-th order neighborhood proximity. While learning from the former exploits
direct user-item associations observable from the graph, learning from the
latter makes use of implicit associations such as user-user similarities and
item-item similarities, which can provide valuable information especially when
the graph is sparse. Moreover, for improving scalability and flexibility, we
propose a sampling technique that is specifically designed to capture the two
types of proximity relations. Extensive experiments on eight benchmark datasets
show that CSE yields significantly better performance than state-of-the-art
recommendation methods.
3D Scene Reconstruction with Multi-layer Depth and Epipolar Transformers
Authors: Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes
Date: 2019-02-18 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Scene Reconstruction

We tackle the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image. This challenging task requires inferring the shape of both visible and occluded surfaces. Our approach utilizes viewer-centered, multi-layer representation of scene geometry adapted from recent methods for single object shape completion. To improve the accuracy of view-centered representations for complex scenes, we introduce a novel "Epipolar Feature Transformer" that transfers convolutional network features from an input view to other virtual camera viewpoints, and thus better covers the 3D scene geometry. Unlike existing approaches that first detect and localize objects in 3D, and then infer object shape using category-specific models, our approach is fully convolutional, end-to-end differentiable, and avoids the resolution and memory limitations of voxel representations. We demonstrate the advantages of multi-layer depth representations and epipolar feature transformers on the reconstruction of a large database of indoor scenes.
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Authors: Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington
Date: 2019-02-18 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Gaussian Processes

A longstanding goal in deep learning research has been to precisely characterize training and generalization. However, the often complex loss landscapes of neural networks have made a theory of learning dynamics elusive. In this work, we show that for wide neural networks the learning dynamics simplify considerably and that, in the infinite width limit, they are governed by a linear model obtained from the first-order Taylor expansion of the network around its initial parameters. Furthermore, mirroring the correspondence between wide Bayesian neural networks and Gaussian processes, gradient-based training of wide neural networks with a squared loss produces test set predictions drawn from a Gaussian process with a particular compositional kernel. While these theoretical results are only exact in the infinite width limit, we nevertheless find excellent empirical agreement between the predictions of the original network and those of the linearized version even for finite practically-sized networks. This agreement is robust across different architectures, optimization methods, and loss functions.
A parallel Fortran framework for neural networks and deep learning
Authors: Milan Curcic
Date: 2019-02-18 00:00:00
Tasks: Deep Learning

This paper describes neural-fortran, a parallel Fortran framework for neural
networks and deep learning. It features a simple interface to construct
feed-forward neural networks of arbitrary structure and size, several
activation functions, and stochastic gradient descent as the default
optimization algorithm. Neural-fortran also leverages the Fortran 2018 standard
collective subroutines to achieve data-based parallelism on shared- or
distributed-memory machines. First, I describe the implementation of neural
networks with Fortran derived types, whole-array arithmetic, and collective sum
and broadcast operations to achieve parallelism. Second, I demonstrate the use
of neural-fortran in an example of recognizing hand-written digits from images.
Finally, I evaluate the computational performance in both serial and parallel
modes. Ease of use and computational performance are similar to an existing
popular machine learning framework, making neural-fortran a viable candidate
for further development and use in production.
Incremental Cluster Validity Indices for Hard Partitions: Extensions and Comparative Study
Authors: Leonardo Enzo Brito da Silva, Niklas M. Melton, Donald C. Wunsch II
Date: 2019-02-18 00:00:00
Tasks: Clustering

Validation is one of the most important aspects of clustering, but most
approaches have been batch methods. Recently, interest has grown in providing
incremental alternatives. This paper extends the incremental cluster validity
index (iCVI) family to include incremental versions of Calinski-Harabasz (iCH),
I index and Pakhira-Bandyopadhyay-Maulik (iI and iPBM), Silhouette (iSIL),
Negentropy Increment (iNI), Representative Cross Information Potential (irCIP)
and Representative Cross Entropy (irH), and Conn_Index (iConn_Index).
Additionally, the effect of under- and over-partitioning on the behavior of
these six iCVIs, the Partition Separation (PS) index, as well as two other
recently developed iCVIs (incremental Xie-Beni (iXB) and incremental
Davies-Bouldin (iDB)) was examined through a comparative study. Experimental
results using fuzzy adaptive resonance theory (ART)-based clustering methods
showed that while evidence of most under-partitioning cases could be inferred
from the behaviors of all these iCVIs, over-partitioning was found to be a more
challenging scenario indicated only by the iConn_Index. The expansion of
incremental validity indices provides significant novel opportunities for
assessing and interpreting the results of unsupervised learning.
On Evaluating Adversarial Robustness
Authors: Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, Alexey Kurakin
Date: 2019-02-18 00:00:00
Tasks: Adversarial Attack, Adversarial Defense, Adversarial Robustness

Correctly evaluating defenses against adversarial examples has proven to be
extremely difficult. Despite the significant amount of recent work attempting
to design defenses that withstand adaptive attacks, few have succeeded; most
papers that propose defenses are quickly shown to be incorrect.
  We believe a large contributing factor is the difficulty of performing
security evaluations. In this paper, we discuss the methodological foundations,
review commonly accepted best practices, and suggest new methods for evaluating
defenses to adversarial examples. We hope that both researchers developing
defenses as well as readers and reviewers who wish to understand the
completeness of an evaluation consider our advice in order to avoid common
pitfalls.
HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
Authors: Swalpa Kumar Roy, Gopal Krishna, Shiv Ram Dubey, Bidyut. B. Chaudhuri
Date: 2019-02-18 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional Neural Network (CNN) is one of the most frequently used deep learning based methods for visual data processing. The use of CNN for HSI classification is also visible in recent works. These approaches are mostly based on 2D CNN. Whereas, the HSI classification performance is highly dependent on both spatial and spectral information. Very few methods have utilized the 3D CNN because of increased computational complexity. This letter proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI classification. Basically, the HybridSN is a spectral-spatial 3D-CNN followed by spatial 2D-CNN. The 3D-CNN facilitates the joint spatial-spectral feature representation from a stack of spectral bands. The 2D-CNN on top of the 3D-CNN further learns more abstract level spatial representation. Moreover, the use of hybrid CNNs reduces the complexity of the model compared to 3D-CNN alone. To test the performance of this hybrid approach, very rigorous HSI classification experiments are performed over Indian Pines, Pavia University and Salinas Scene remote sensing datasets. The results are compared with the state-of-the-art hand-crafted as well as end-to-end deep learning based methods. A very satisfactory performance is obtained using the proposed HybridSN for HSI classification. The source code can be found at \url{https://github.com/gokriznastic/HybridSN}.
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data
Authors: Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava
Date: 2019-02-18 00:00:00
Tasks: compressed sensing, Density Estimation

We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset. Our online sketching algorithm compresses an N element dataset to a sketch of size $O(N^b \log^3 N)$ in $O(N^{(b+1)} \log^3 N)$ time, where $b < 1$. This sketch can correctly report the nearest neighbors of any query that satisfies a stability condition parameterized by $b$. We achieve sublinear memory performance on stable queries by combining recent advances in locality sensitive hash (LSH)-based estimators, online kernel density estimation, and compressed sensing. Our theoretical results shed new light on the memory-accuracy tradeoff for nearest neighbor search, and our sketch, which consists entirely of short integer arrays, has a variety of attractive features in practice. We evaluate the memory-recall tradeoff of our method on a friend recommendation task in the Google Plus social media network. We obtain orders of magnitude better compression than the random projection based alternative while retaining the ability to report the nearest neighbors of practical queries.
Generative Adversarial Networks Synthesize Realistic OCT Images of the Retina
Authors: Stephen G. Odaibo, M. D., M. S.
Date: 2019-02-18 00:00:00

We report, to our knowledge, the first end-to-end application of Generative
Adversarial Networks (GANs) towards the synthesis of Optical Coherence
Tomography (OCT) images of the retina. Generative models have gained recent
attention for the increasingly realistic images they can synthesize, given a
sampling of a data type. In this paper, we apply GANs to a sampling
distribution of OCTs of the retina. We observe the synthesis of realistic OCT
images depicting recognizable pathology such as macular holes, choroidal
neovascular membranes, myopic degeneration, cystoid macular edema, and central
serous retinopathy amongst others. This represents the first such report of its
kind. Potential applications of this new technology include for surgical
simulation, for treatment planning, for disease prognostication, and for
accelerating the development of new drugs and surgical procedures to treat
retinal disease.
Is a single unique Bayesian network enough to accurately represent your data?
Authors: Gilles Kratzer, Reinhard Furrer
Date: 2019-02-18 00:00:00
Tasks: Epidemiology

Bayesian network (BN) modelling is extensively used in systems epidemiology.
Usually it consists in selecting and reporting the best-fitting structure
conditional to the data. A major practical concern is avoiding overfitting, on
account of its extreme flexibility and its modelling richness. Many approaches
have been proposed to control for overfitting. Unfortunately, they essentially
all rely on very crude decisions that result in too simplistic approaches for
such complex systems. In practice, with limited data sampled from complex
system, this approach seems too simplistic. An alternative would be to use the
Monte Carlo Markov chain model choice (MC3) over the network to learn the
landscape of reasonably supported networks, and then to present all possible
arcs with their MCMC support. This paper presents an R implementation, called
mcmcabn, of a flexible structural MC3 that is accessible to non-specialists.
Investigating the Effect of Segmentation Methods on Neural Model based Sentiment Analysis on Informal Short Texts in Turkish
Authors: Fatih Kurt, Dilek Kisa, Pinar Karagoz
Date: 2019-02-18 00:00:00
Tasks: General Classification, Segmentation, Sentiment Analysis, Sentiment Classification

This work investigates segmentation approaches for sentiment analysis on
informal short texts in Turkish. The two building blocks of the proposed work
are segmentation and deep neural network model. Segmentation focuses on
preprocessing of text with different methods. These methods are grouped in
four: morphological, sub-word, tokenization, and hybrid approaches. We analyzed
several variants for each of these four methods. The second stage focuses on
evaluation of the neural model for sentiment analysis. The performance of each
segmentation method is evaluated under Convolutional Neural Network (CNN) and
Recurrent Neural Network (RNN) model proposed in the literature for sentiment
classification.
Appendix for: Cut-free Calculi and Relational Semantics for Temporal STIT logics
Authors: Kees van Berkel, Tim Lyon
Date: 2019-02-18 00:00:00

This paper is an appendix to the paper "Cut-free Calculi and Relational
Semantics for Temporal STIT logics" by Berkel and Lyon, 2019. It provides the
completeness proof for the basic STIT logic Ldm (relative to irreflexive,
temporal Kripke STIT frames) as well as gives the derivation of the
independence of agents axiom for the logic Xstit.