pwc-archive/papers-with-abstracts huggingface dataset:
A Greedy Approach to $\ell_{0,\infty}$ Based Convolutional Sparse Coding
Authors: Elad Plaut, Raja Giryes
Date: 2018-12-26 00:00:00
Tasks: Dictionary Learning, Image Inpainting, Salt-And-Pepper Noise Removal

Sparse coding techniques for image processing traditionally rely on a
processing of small overlapping patches separately followed by averaging. This
has the disadvantage that the reconstructed image no longer obeys the sparsity
prior used in the processing. For this purpose convolutional sparse coding has
been introduced, where a shift-invariant dictionary is used and the sparsity of
the recovered image is maintained. Most such strategies target the $\ell_0$
"norm" or the $\ell_1$ norm of the whole image, which may create an imbalanced
sparsity across various regions in the image. In order to face this challenge,
the $\ell_{0,\infty}$ "norm" has been proposed as an alternative that "operates
locally while thinking globally". The approaches taken for tackling the
non-convexity of these optimization problems have been either using a convex
relaxation or local pursuit algorithms. In this paper, we present an efficient
greedy method for sparse coding and dictionary learning, which is specifically
tailored to $\ell_{0,\infty}$, and is based on matching pursuit. We demonstrate
the usage of our approach in salt-and-pepper noise removal and image
inpainting. A code package which reproduces the experiments presented in this
work is available at https://web.eng.tau.ac.il/~raja
Prediction of Industrial Process Parameters using Artificial Intelligence Algorithms
Authors: Abdelmoula Khdoudi, Tawfik Masrour
Date: 2018-12-26 00:00:00
Tasks: Prediction, regression

In the present paper, a method of defining the industrial process parameters
for a new product using machine learning algorithms will be presented. The
study will describe how to go from the product characteristics till the
prediction of the suitable machine parameters to produce a good quality of this
product, and this is based on an historical training dataset of similar
products with their respective process parameters. In the first part of our
study, we will focus on the ultrasonic welding process definition, welding
parameters and on how it operate. While in second part, we present the design
and implementation of the prediction models such multiple linear regression,
support vector regression, and we compare them to an artificial neural networks
algorithm. In the following part, we present a new application of Convolutional
Neural Networks (CNN) to the industrial process parameters prediction. In
addition, we will propose the generalization approach of our CNN to any
prediction problem of industrial process parameters. Finally the results of the
four methods will be interpreted and discussed.
A Unified Learning Based Framework for Light Field Reconstruction from Coded Projections
Authors: Anil Kumar Vadathya, Sharath Girish, Kaushik Mitra
Date: 2018-12-26 00:00:00
Tasks: All

Light field presents a rich way to represent the 3D world by capturing the spatio-angular dimensions of the visual signal. However, the popular way of capturing light field (LF) via a plenoptic camera presents spatio-angular resolution trade-off. Computational imaging techniques such as compressive light field and programmable coded aperture reconstruct full sensor resolution LF from coded projections obtained by multiplexing the incoming spatio-angular light field. Here, we present a unified learning framework that can reconstruct LF from a variety of multiplexing schemes with minimal number of coded images as input. We consider three light field capture schemes: heterodyne capture scheme with code placed near the sensor, coded aperture scheme with code at the camera aperture and finally the dual exposure scheme of capturing a focus-defocus pair where there is no explicit coding. Our algorithm consists of three stages 1) we recover the all-in-focus image from the coded image 2) we estimate the disparity maps for all the LF views from the coded image and the all-in-focus image, 3) we then render the LF by warping the all-in-focus image using disparity maps and refine it. For these three stages we propose three deep neural networks - ViewNet, DispairtyNet and RefineNet. Our reconstructions show that our learning algorithm achieves state-of-the-art results for all the three multiplexing schemes. Especially, our LF reconstructions from focus-defocus pair is comparable to other learning-based view synthesis approaches from multiple images. Thus, our work paves the way for capturing high-resolution LF (~ a megapixel) using conventional cameras such as DSLRs. Please check our supplementary materials $\href{https://docs.google.com/presentation/d/1Vr-F8ZskrSd63tvnLfJ2xmEXY6OBc1Rll3XeOAtc11I/}{online}$ to better appreciate the reconstructed light fields.
Adversarial Attack and Defense on Graph Data: A Survey
Authors: Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Yixin Liu, Philip S. Yu, Lifang He, Bo Li
Date: 2018-12-26 00:00:00
Tasks: Adversarial Attack, image-classification, Image Classification, Survey, Text Generation

Deep neural networks (DNNs) have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works about adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph data due to its representation structure. Given the importance of graph analysis, an increasing number of studies over the past few years have attempted to analyze the robustness of machine learning models on graph data. Nevertheless, existing research considering adversarial behaviors on graph data often focuses on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation, which makes the comparison among different methods difficult. Therefore, this review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models. Moreover, we also compare different graph attacks and defenses along with their contributions and limitations, as well as summarize the evaluation metrics, datasets and future trends. We hope this survey can help fill the gap in the literature and facilitate further development of this promising new field.
Exploring the Challenges towards Lifelong Fact Learning
Authors: Mohamed Elhoseiny, Francesca Babiloni, Rahaf Aljundi, Marcus Rohrbach, Manohar Paluri, Tinne Tuytelaars
Date: 2018-12-26 00:00:00

So far life-long learning (LLL) has been studied in relatively small-scale
and relatively artificial setups. Here, we introduce a new large-scale
alternative. What makes the proposed setup more natural and closer to
human-like visual systems is threefold: First, we focus on concepts (or facts,
as we call them) of varying complexity, ranging from single objects to more
complex structures such as objects performing actions, and objects interacting
with other objects. Second, as in real-world settings, our setup has a
long-tail distribution, an aspect which has mostly been ignored in the LLL
context. Third, facts across tasks may share structure (e.g., <person, riding,
wave> and <dog, riding, wave>). Facts can also be semantically related (e.g.,
"liger" relates to seen categories like "tiger" and "lion"). Given the large
number of possible facts, a LLL setup seems a natural choice. To avoid model
size growing over time and to optimally exploit the semantic relations and
structure, we combine it with a visual semantic embedding instead of discrete
class labels. We adapt existing datasets with the properties mentioned above
into new benchmarks, by dividing them semantically or randomly into disjoint
tasks. This leads to two large-scale benchmarks with 906,232 images and 165,150
unique facts, on which we evaluate and analyze state-of-the-art LLL methods.
Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
Authors: Jesús Arroyo, Daniel L. Sussman, Carey E. Priebe, Vince Lyzinski
Date: 2018-12-26 00:00:00
Tasks: Graph Matching

Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements. We study this problem from a statistical framework in which one of the graphs is an errorfully observed copy of the other. We introduce a corrupting channel model, and show that in this model framework, the solution to the graph matching problem is a maximum likelihood estimator. Necessary and sufficient conditions for consistency of this MLE are presented, as well as a relaxed notion of consistency in which a negligible fraction of the vertices need not be matched correctly. The results are used to study matchability in several families of random graphs, including edge independent models, random regular graphs and small-world networks. We also use these results to introduce measures of matching feasibility, and experimentally validate the results on simulated and real-world networks.
Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine
Authors: Han Zhang, Bo Ai, Wenjun Xu, Li Xu, Shuguang Cui
Date: 2018-12-26 00:00:00

Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms. In this letter, a modified extreme learning machine (ELM) that can process tensorial data, or ELM model with tensorial inputs (TELM), is proposed to handle the channel interpolation task. The TELM inherits many good properties from ELMs. Based on the TELM, the Tucker decomposed extreme learning machine (TDELM) is proposed for further improving the performance. Furthermore, we establish a theoretical argument to measure the interpolation capability of the proposed learning machines. Experimental results verify that our proposed learning machines can achieve comparable mean squared error (MSE) performance against the traditional ELMs but with 15% shorter running time, and outperform the other methods for a 20% margin measured in MSE for channel interpolation.
Learning to Walk via Deep Reinforcement Learning
Authors: Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine
Date: 2018-12-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domain of robotic locomotion, deep RL could enable learning locomotion skills with minimal engineering and without an explicit model of the robot dynamics. Unfortunately, applying deep RL to real-world robotic tasks is exceptionally difficult, primarily due to poor sample complexity and sensitivity to hyperparameters. While hyperparameters can be easily tuned in simulated domains, tuning may be prohibitively expensive on physical systems, such as legged robots, that can be damaged through extensive trial-and-error learning. In this paper, we propose a sample-efficient deep RL algorithm based on maximum entropy RL that requires minimal per-task tuning and only a modest number of trials to learn neural network policies. We apply this method to learning walking gaits on a real-world Minitaur robot. Our method can acquire a stable gait from scratch directly in the real world in about two hours, without relying on any model or simulation, and the resulting policy is robust to moderate variations in the environment. We further show that our algorithm achieves state-of-the-art performance on simulated benchmarks with a single set of hyperparameters. Videos of training and the learned policy can be found on the project website.
Hierarchical LSTMs with Adaptive Attention for Visual Captioning
Authors: Jingkuan Song, Xiangpeng Li, Lianli Gao, Heng Tao Shen
Date: 2018-12-26 00:00:00
Tasks: Caption Generation, Image Captioning, Language Modeling, Language Modelling, Video Captioning

Recent progress has been made in using attention based encoder-decoder
framework for image and video captioning. Most existing decoders apply the
attention mechanism to every generated word including both visual words (e.g.,
"gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these
non-visual words can be easily predicted using natural language model without
considering visual signals or attention. Imposing attention mechanism on
non-visual words could mislead and decrease the overall performance of visual
captioning. Furthermore, the hierarchy of LSTMs enables more complex
representation of visual data, capturing information at different scales. To
address these issues, we propose a hierarchical LSTM with adaptive attention
(hLSTMat) approach for image and video captioning. Specifically, the proposed
framework utilizes the spatial or temporal attention for selecting specific
regions or frames to predict the related words, while the adaptive attention is
for deciding whether to depend on the visual information or the language
context information. Also, a hierarchical LSTMs is designed to simultaneously
consider both low-level visual information and high-level language context
information to support the caption generation. We initially design our hLSTMat
for video captioning task. Then, we further refine it and apply it to image
captioning task. To demonstrate the effectiveness of our proposed framework, we
test our method on both video and image captioning tasks. Experimental results
show that our approach achieves the state-of-the-art performance for most of
the evaluation metrics on both tasks. The effect of important components is
also well exploited in the ablation study.
Multi-resolution neural networks for tracking seismic horizons from few training images
Authors: Bas Peters, Justin Granek, Eldad Haber
Date: 2018-12-26 00:00:00

Detecting a specific horizon in seismic images is a valuable tool for
geological interpretation. Because hand-picking the locations of the horizon is
a time-consuming process, automated computational methods were developed
starting three decades ago. Older techniques for such picking include
interpolation of control points however, in recent years neural networks have
been used for this task. Until now, most networks trained on small patches from
larger images. This limits the networks ability to learn from large-scale
geologic structures. Moreover, currently available networks and training
strategies require label patches that have full and continuous annotations,
which are also time-consuming to generate.
  We propose a projected loss-function for training convolutional networks with
a multi-resolution structure, including variants of the U-net. Our networks
learn from a small number of large seismic images without creating patches. The
projected loss-function enables training on labels with just a few annotated
pixels and has no issue with the other unknown label pixels. Training uses all
data without reserving some for validation. Only the labels are split into
training/testing. Contrary to other work on horizon tracking, we train the
network to perform non-linear regression, and not classification. As such, we
propose labels as the convolution of a Gaussian kernel and the known horizon
locations that indicate uncertainty in the labels. The network output is the
probability of the horizon location. We demonstrate the proposed computational
ingredients on two different datasets, for horizon extrapolation and
interpolation. We show that the predictions of our methodology are accurate
even in areas far from known horizon locations because our learning strategy
exploits all data in large seismic images.
Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GAN
Authors: Shervin Minaee, Amirali Abdolrashidi
Date: 2018-12-25 00:00:00
Tasks: Diversity

Generating realistic biometric images has been an interesting and, at the
same time, challenging problem. Classical statistical models fail to generate
realistic-looking fingerprint images, as they are not powerful enough to
capture the complicated texture representation in fingerprint images. In this
work, we present a machine learning framework based on generative adversarial
networks (GAN), which is able to generate fingerprint images sampled from a
prior distribution (learned from a set of training images). We also add a
suitable regularization term to the loss function, to impose the connectivity
of generated fingerprint images. This is highly desirable for fingerprints, as
the lines in each finger are usually connected. We apply this framework to two
popular fingerprint databases, and generate images which look very realistic,
and similar to the samples in those databases. Through experimental results, we
show that the generated fingerprint images have a good diversity, and are able
to capture different parts of the prior distribution. We also evaluate the
Frechet Inception distance (FID) of our proposed model, and show that our model
is able to achieve good quantitative performance in terms of this score.
Multimodal deep learning for short-term stock volatility prediction
Authors: Marcelo Sardelich, Suresh Manandhar
Date: 2018-12-25 00:00:00
Tasks: Deep Learning, Multimodal Deep Learning, Prediction, Sentence

Stock market volatility forecasting is a task relevant to assessing market
risk. We investigate the interaction between news and prices for the
one-day-ahead volatility prediction using state-of-the-art deep learning
approaches. The proposed models are trained either end-to-end or using sentence
encoders transfered from other tasks. We evaluate a broad range of stock market
sectors, namely Consumer Staples, Energy, Utilities, Heathcare, and Financials.
Our experimental results show that adding news improves the volatility
forecasting as compared to the mainstream models that rely only on price data.
In particular, our model outperforms the widely-recognized GARCH(1,1) model for
all sectors in terms of coefficient of determination $R^2$, $MSE$ and $MAE$,
achieving the best performance when training from both news and price data.
Residual Dense Network for Image Restoration
Authors: Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, Yun Fu
Date: 2018-12-25 00:00:00
Tasks: Deblurring, Denoising, Image Compression, Image Compression Artifact Reduction, Image Deblurring, Image Denoising, Image Restoration, Image Super-Resolution, JPEG Artifact Correction, Super-Resolution

Convolutional neural network has recently achieved great success for image restoration (IR) and also offered hierarchical features. However, most deep CNN based IR models do not make full use of the hierarchical features from the original low-quality images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in IR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via densely connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory mechanism. To adaptively learn more effective features from preceding and current local features and stabilize the training of wider network, we proposed local feature fusion in RDB. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. We demonstrate the effectiveness of RDN with several representative IR applications, single image super-resolution, Gaussian image denoising, image compression artifact reduction, and image deblurring. Experiments on benchmark and real-world datasets show that our RDN achieves favorable performance against state-of-the-art methods for each IR task quantitatively and visually.
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense
Authors: Hang Zhou, Kejiang Chen, Weiming Zhang, Han Fang, Wenbo Zhou, Nenghai Yu
Date: 2018-12-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Denoising, Point Cloud Classification

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose a Denoiser and UPsampler Network (DUP-Net) structure as defenses for 3D adversarial point cloud classification, where the two modules reconstruct surface smoothness by dropping or adding points. In this paper, statistical outlier removal (SOR) and a data-driven upsampling network are considered as denoiser and upsampler respectively. Compared with baseline defenses, DUP-Net has three advantages. First, with DUP-Net as a defense, the target model is more robust to white-box adversarial attacks. Second, the statistical outlier removal provides added robustness since it is a non-differentiable denoising operation. Third, the upsampler network can be trained on a small dataset and defends well against adversarial attacks generated from other point cloud datasets. We conduct various experiments to validate that DUP-Net is very effective as defense in practice. Our best defense eliminates 83.8% of C&W and l_2 loss based attack (point shifting), 50.0% of C&W and Hausdorff distance loss based attack (point adding) and 9.0% of saliency map based attack (point dropping) under 200 dropped points on PointNet.
Learning from past scans: Tomographic reconstruction to detect new structures
Authors: Preeti Gopal, Sharat Chandran, Imants Svalbe, Ajit Rajwade
Date: 2018-12-23 00:00:00

The need for tomographic reconstruction from sparse measurements arises when
the measurement process is potentially harmful, needs to be rapid, or is
uneconomical. In such cases, prior information from previous longitudinal scans
of the same or similar objects helps to reconstruct the current object whilst
requiring significantly fewer `updating' measurements. However, a significant
limitation of all prior-based methods is the possible dominance of the prior
over the reconstruction of new localised information that has evolved within
the test object. In this paper, we improve the state of the art by (1)
detecting potential regions where new changes may have occurred, and (2)
effectively reconstructing both the old and new structures by computing
regional weights that moderate the local influence of the priors. We have
tested the efficacy of our method on synthetic as well as real volume data. The
results demonstrate that using weighted priors significantly improves the
overall quality of the reconstructed data whilst minimising their impact on
regions that contain new information.
Deep Ptych: Subsampled Fourier Ptychography using Generative Priors
Authors: Fahad Shamshad, Farwa Abbas, Ali Ahmed
Date: 2018-12-22 00:00:00

This paper proposes a novel framework to regularize the highly ill-posed and
non-linear Fourier ptychography problem using generative models. We demonstrate
experimentally that our proposed algorithm, Deep Ptych, outperforms the
existing Fourier ptychography techniques, in terms of quality of reconstruction
and robustness against noise, using far fewer samples. We further modify the
proposed approach to allow the generative model to explore solutions outside
the range, leading to improved performance.