pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces
Authors: Ziv Katzir, Yuval Elovici
Date: 2018-11-22 00:00:00
Tasks: General Classification, image-classification, Image Classification

Neural network based classifiers are still prone to manipulation through
adversarial perturbations. State of the art attacks can overcome most of the
defense or detection mechanisms suggested so far, and adversaries have the
upper hand in this arms race. Adversarial examples are designed to resemble the
normal input from which they were constructed, while triggering an incorrect
classification. This basic design goal leads to a characteristic spatial
behavior within the context of Activation Spaces, a term coined by the authors
to refer to the hyperspaces formed by the activation values of the network's
layers. Within the output of the first layers of the network, an adversarial
example is likely to resemble normal instances of the source class, while in
the final layers such examples will diverge towards the adversary's target
class. The steps below enable us to leverage this inherent shift from one class
to another in order to form a novel adversarial example detector. We construct
Euclidian spaces out of the activation values of each of the deep neural
network layers. Then, we induce a set of k-nearest neighbor classifiers (k-NN),
one per activation space of each neural network layer, using the
non-adversarial examples. We leverage those classifiers to produce a sequence
of class labels for each nonperturbed input sample and estimate the a priori
probability for a class label change between one activation space and another.
During the detection phase we compute a sequence of classification labels for
each input using the trained classifiers. We then estimate the likelihood of
those classification sequences and show that adversarial sequences are far less
likely than normal ones. We evaluated our detection method against the state of
the art C&W attack method, using two image classification datasets (MNIST,
CIFAR-10) reaching an AUC 0f 0.95 for the CIFAR-10 dataset.
Super Diffusion for Salient Object Detection
Authors: Peng Jiang, Zhiyi Pan, Nuno Vasconcelos, Baoquan Chen, Jingliang Peng
Date: 2018-11-22 00:00:00
Tasks: Clustering, Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

One major branch of saliency object detection methods is diffusion-based
which construct a graph model on a given image and diffuse seed saliency values
to the whole graph by a diffusion matrix. While their performance is sensitive
to specific feature spaces and scales used for the diffusion matrix definition,
little work has been published to systematically promote the robustness and
accuracy of salient object detection under the generic mechanism of diffusion.
In this work, we firstly present a novel view of the working mechanism of the
diffusion process based on mathematical analysis, which reveals that the
diffusion process is actually computing the similarity of nodes with respect to
the seeds based on diffusion maps. Following this analysis, we propose super
diffusion, a novel inclusive learning-based framework for salient object
detection, which makes the optimum and robust performance by integrating a
large pool of feature spaces, scales and even features originally computed for
non-diffusion-based salient object detection. A closed-form solution of the
optimal parameters for the integration is determined through supervised
learning. At the local level, we propose to promote each individual diffusion
before the integration. Our mathematical analysis reveals the close
relationship between saliency diffusion and spectral clustering. Based on this,
we propose to re-synthesize each individual diffusion matrix from the most
discriminative eigenvectors and the constant eigenvector (for saliency
normalization). The proposed framework is implemented and experimented on
prevalently used benchmark datasets, consistently leading to state-of-the-art
performance.
KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
Authors: Isidro Cortes Ciriano, Andreas Bender
Date: 2018-11-22 00:00:00
Tasks: Drug Discovery, Prediction Of Cancer Cell Line Sensitivity, Sensitivity

The application of convolutional neural networks (ConvNets) to harness high-content screening images or 2D compound representations is gaining increasing attention in drug discovery. However, existing applications often require large data sets for training, or sophisticated pretraining schemes. Here, we show using 33 IC50 data sets from ChEMBL 23 that the in vitro activity of compounds on cancer cell lines and protein targets can be accurately predicted on a continuous scale from their Kekule structure representations alone by extending existing architectures, which were pretrained on unrelated image data sets. We show that the predictive power of the generated models is comparable to that of Random Forest (RF) models and fully-connected Deep Neural Networks trained on circular (Morgan) fingerprints. Notably, including additional fully-connected layers further increases the predictive power of the ConvNets by up to 10%. Analysis of the predictions generated by RF models and ConvNets shows that by simply averaging the output of the RF models and ConvNets we obtain significantly lower errors in prediction for multiple data sets, although the effect size is small, than those obtained with either model alone, indicating that the features extracted by the convolutional layers of the ConvNets provide complementary predictive signal to Morgan fingerprints. Lastly, we show that multi-task ConvNets trained on compound images permit to model COX isoform selectivity on a continuous scale with errors in prediction comparable to the uncertainty of the data. Overall, in this work we present a set of ConvNet architectures for the prediction of compound activity from their Kekule structure representations with state-of-the-art performance, that require no generation of compound descriptors or use of sophisticated image processing techniques.
Data Augmentation using Random Image Cropping and Patching for Deep CNNs
Authors: Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara
Date: 2018-11-22 00:00:00
Tasks: Data Augmentation, Image Augmentation, Image Cropping, Retrieval

Deep convolutional neural networks (CNNs) have achieved remarkable results in image processing tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. Recent CNN architectures with more parameters are rendering traditional data augmentation techniques insufficient. In this study, we propose a new data augmentation technique called random image cropping and patching (RICAP) which randomly crops four images and patches them to create a new training image. Moreover, RICAP mixes the class labels of the four images, resulting in an advantage similar to label smoothing. We evaluated RICAP with current state-of-the-art CNNs (e.g., the shake-shake regularization model) by comparison with competitive data augmentation techniques such as cutout and mixup. RICAP achieves a new state-of-the-art test error of $2.19\%$ on CIFAR-10. We also confirmed that deep CNNs with RICAP achieve better results on classification tasks using CIFAR-100 and ImageNet and an image-caption retrieval task using Microsoft COCO.
Bandits with Temporal Stochastic Constraints
Authors: Priyank Agrawal, Theja Tulabandhula
Date: 2018-11-22 00:00:00
Tasks: Multi-Armed Bandits

We study the effect of impairment on stochastic multi-armed bandits and develop new ways to mitigate it. Impairment effect is the phenomena where an agent only accrues reward for an action if they have played it at least a few times in the recent past. It is practically motivated by repetition and recency effects in domains such as advertising (here consumer behavior may require repeat actions by advertisers) and vocational training (here actions are complex skills that can only be mastered with repetition to get a payoff). Impairment can be naturally modelled as a temporal constraint on the strategy space, and we provide two novel algorithms that achieve sublinear regret, each working with different assumptions on the impairment effect. We introduce a new notion called bucketing in our algorithm design, and show how it can effectively address impairment as well as a broader class of temporal constraints. Our regret bounds explicitly capture the cost of impairment and show that it scales (sub-)linearly with the degree of impairment. Our work complements recent work on modeling delays and corruptions, and we provide experimental evidence supporting our claims.
Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks
Authors: Ashkan Abbasi, Amirhassan Monadjemi, Leyuan Fang, Hossein Rabbani, Yi Zhang
Date: 2018-11-22 00:00:00
Tasks: Denoising, Image Denoising, Super-Resolution

In recent years, there has been a growing interest in applying convolutional
neural networks (CNNs) to low-level vision tasks such as denoising and
super-resolution. Due to the coherent nature of the image formation process,
optical coherence tomography (OCT) images are inevitably affected by noise.
This paper proposes a new method named the multi-input fully-convolutional
networks (MIFCN) for denoising of OCT images. In contrast to recently proposed
natural image denoising CNNs, the proposed architecture allows the exploitation
of high degrees of correlation and complementary information among neighboring
OCT images through pixel by pixel fusion of multiple FCNs. The parameters of
the proposed multi-input architecture are learned by considering the
consistency between the overall output and the contribution of each input
image. The proposed MIFCN method is compared with the state-of-the-art
denoising methods adopted on OCT images of normal and age-related macular
degeneration eyes in a quantitative and qualitative manner.
Task-generalizable Adversarial Attack based on Perceptual Metric
Authors: Muzammal Naseer, Salman H. Khan, Shafin Rahman, Fatih Porikli
Date: 2018-11-22 00:00:00
Tasks: Adversarial Attack, object-detection, Object Detection, Object Recognition

Deep neural networks (DNNs) can be easily fooled by adding human
imperceptible perturbations to the images. These perturbed images are known as
`adversarial examples' and pose a serious threat to security and safety
critical systems. A litmus test for the strength of adversarial examples is
their transferability across different DNN models in a black box setting (i.e.
when the target model's architecture and parameters are not known to attacker).
Current attack algorithms that seek to enhance adversarial transferability work
on the decision level i.e. generate perturbations that alter the network
decisions. This leads to two key limitations: (a) An attack is dependent on the
task-specific loss function (e.g. softmax cross-entropy for object recognition)
and therefore does not generalize beyond its original task. (b) The adversarial
examples are specific to the network architecture and demonstrate poor
transferability to other network architectures. We propose a novel approach to
create adversarial examples that can broadly fool different networks on
multiple tasks. Our approach is based on the following intuition: "Perpetual
metrics based on neural network features are highly generalizable and show
excellent performance in measuring and stabilizing input distortions. Therefore
an ideal attack that creates maximum distortions in the network feature space
should realize highly transferable examples". We report extensive experiments
to show how adversarial examples generalize across multiple networks for
classification, object detection and segmentation tasks.
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
Authors: Yibing Song, Jiawei Zhang, Lijun Gong, Shengfeng He, Linchao Bao, Jinshan Pan, Qingxiong Yang, Ming-Hsuan Yang
Date: 2018-11-22 00:00:00
Tasks: Deblurring, Face Hallucination, Hallucination, Super-Resolution

We address the problem of restoring a high-resolution face image from a
blurry low-resolution input. This problem is difficult as super-resolution and
deblurring need to be tackled simultaneously. Moreover, existing algorithms
cannot handle face images well as low-resolution face images do not have much
texture which is especially critical for deblurring. In this paper, we propose
an effective algorithm by utilizing the domain-specific knowledge of human
faces to recover high-quality faces. We first propose a facial component guided
deep Convolutional Neural Network (CNN) to restore a coarse face image, which
is denoted as the base image where the facial component is automatically
generated from the input face image. However, the CNN based method cannot
handle image details well. We further develop a novel exemplar-based detail
enhancement algorithm via facial component matching. Extensive experiments show
that the proposed method outperforms the state-of-the-art algorithms both
quantitatively and qualitatively.
An Off-policy Policy Gradient Theorem Using Emphatic Weightings
Authors: Ehsan Imani, Eric Graves, Martha White
Date: 2018-11-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Policy Gradient Methods, Reinforcement Learning

Policy gradient methods are widely used for control in reinforcement learning, particularly for the continuous action setting. There have been a host of theoretically sound algorithms proposed for the on-policy setting, due to the existence of the policy gradient theorem which provides a simplified form for the gradient. In off-policy learning, however, where the behaviour policy is not necessarily attempting to learn and follow the optimal policy for the given task, the existence of such a theorem has been elusive. In this work, we solve this open problem by providing the first off-policy policy gradient theorem. The key to the derivation is the use of $emphatic$ $weightings$. We develop a new actor-critic algorithm$\unicode{x2014}$called Actor Critic with Emphatic weightings (ACE)$\unicode{x2014}$that approximates the simplified gradients provided by the theorem. We demonstrate in a simple counterexample that previous off-policy policy gradient methods$\unicode{x2014}$particularly OffPAC and DPG$\unicode{x2014}$converge to the wrong solution whereas ACE finds the optimal solution.
Multi-View Inpainting for RGB-D Sequence
Authors: Feiran Li, Gustavo Alfonso Garcia Ricardez, Jun Takamatsu, Tsukasa Ogasawara
Date: 2018-11-22 00:00:00
Tasks: 3D Reconstruction

In this work we propose a novel approach to remove undesired objects from
RGB-D sequences captured with freely moving cameras, which enables static 3D
reconstruction. Our method jointly uses existing information from multiple
frames as well as generates new one via inpainting techniques. We use balanced
rules to select source frames; local homography based image warping method for
alignment and Markov random field (MRF) based approach for combining existing
information. For the left holes, we employ exemplar based multi-view inpainting
method to deal with the color image and coherently use it as guidance to
complete the depth correspondence. Experiments show that our approach is
qualified for removing the undesired objects and inpainting the holes.
Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective
Authors: Zhong-Qiu Wang, Ke Tan, DeLiang Wang
Date: 2018-11-22 00:00:00
Tasks: Speaker Separation

This study investigates phase reconstruction for deep learning based monaural
talker-independent speaker separation in the short-time Fourier transform
(STFT) domain. The key observation is that, for a mixture of two sources, with
their magnitudes accurately estimated and under a geometric constraint, the
absolute phase difference between each source and the mixture can be uniquely
determined; in addition, the source phases at each time-frequency (T-F) unit
can be narrowed down to only two candidates. To pick the right candidate, we
propose three algorithms based on iterative phase reconstruction, group delay
estimation, and phase-difference sign prediction. State-of-the-art results are
obtained on the publicly available wsj0-2mix and 3mix corpus.
Towards Robust Neural Networks with Lipschitz Continuity
Authors: Muhammad Usama, Dong Eui Chang
Date: 2018-11-22 00:00:00
Tasks: Data Augmentation

Deep neural networks have shown remarkable performance across a wide range of
vision-based tasks, particularly due to the availability of large-scale
datasets for training and better architectures. However, data seen in the real
world are often affected by distortions that not accounted for by the training
datasets. In this paper, we address the challenge of robustness and stability
of neural networks and propose a general training method that can be used to
make the existing neural network architectures more robust and stable to input
visual perturbations while using only available datasets for training. Proposed
training method is convenient to use as it does not require data augmentation
or changes in the network architecture. We provide theoretical proof as well as
empirical evidence for the efficiency of the proposed training method by
performing experiments with existing neural network architectures and
demonstrate that same architecture when trained with the proposed training
method perform better than when trained with conventional training approach in
the presence of noisy datasets.
On a Sparse Shortcut Topology of Artificial Neural Networks
Authors: Fenglei Fan, Dayang Wang, Hengtao Guo, Qikui Zhu, Pingkun Yan, Ge Wang, Hengyong Yu
Date: 2018-11-22 00:00:00

In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effectiveness of shortcuts, there remain open questions on the mechanism and characteristics. For example, why are shortcuts powerful? Why do shortcuts generalize well? In this paper, we investigate the expressivity and generalizability of a novel sparse shortcut topology. First, we demonstrate that this topology can empower a one-neuron-wide deep network to approximate any univariate continuous function. Then, we present a novel width-bounded universal approximator in contrast to depth-bounded universal approximators and extend the approximation result to a family of equally competent networks. Furthermore, with generalization bound theory, we show that the proposed shortcut topology enjoys excellent generalizability. Finally, we corroborate our theoretical analyses by comparing the proposed topology with popular architectures, including ResNet and DenseNet, on well-known benchmarks and perform a saliency map analysis to interpret the proposed topology. Our work helps enhance the understanding of the role of shortcuts and suggests further opportunities to innovate neural architectures.
HyperAdam: A Learnable Task-Adaptive Adam for Network Training
Authors: Shipeng Wang, Jian Sun, Zongben Xu
Date: 2018-11-22 00:00:00
Tasks: Stochastic Optimization

Deep neural networks are traditionally trained using human-designed
stochastic optimization algorithms, such as SGD and Adam. Recently, the
approach of learning to optimize network parameters has emerged as a promising
research topic. However, these learned black-box optimizers sometimes do not
fully utilize the experience in human-designed optimizers, therefore have
limitation in generalization ability. In this paper, a new optimizer, dubbed as
\textit{HyperAdam}, is proposed that combines the idea of "learning to
optimize" and traditional Adam optimizer. Given a network for training, its
parameter update in each iteration generated by HyperAdam is an adaptive
combination of multiple updates generated by Adam with varying decay rates. The
combination weights and decay rates in HyperAdam are adaptively learned
depending on the task. HyperAdam is modeled as a recurrent neural network with
AdamCell, WeightCell and StateCell. It is justified to be state-of-the-art for
various network training, such as multilayer perceptron, CNN and LSTM.
Markov Chain Block Coordinate Descent
Authors: Tao Sun, Yuejiao Sun, Yangyang Xu, Wotao Yin
Date: 2018-11-22 00:00:00
Tasks: Distributed Optimization

The method of block coordinate gradient descent (BCD) has been a powerful
method for large-scale optimization. This paper considers the BCD method that
successively updates a series of blocks selected according to a Markov chain.
This kind of block selection is neither i.i.d. random nor cyclic. On the other
hand, it is a natural choice for some applications in distributed optimization
and Markov decision process, where i.i.d. random and cyclic selections are
either infeasible or very expensive. By applying mixing-time properties of a
Markov chain, we prove convergence of Markov chain BCD for minimizing Lipschitz
differentiable functions, which can be nonconvex. When the functions are convex
and strongly convex, we establish both sublinear and linear convergence rates,
respectively. We also present a method of Markov chain inertial BCD. Finally,
we discuss potential applications.
Supervised Fitting of Geometric Primitives to 3D Point Clouds
Authors: Lingxiao Li, Minhyuk Sung, Anastasia Dubrovina, Li Yi, Leonidas Guibas
Date: 2018-11-22 00:00:00
Proceeding: CVPR 2019 6
Tasks: Shape Representation Of 3D Point Clouds

Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized 3D data and high-level structural information on the underlying 3D shapes. As such, it enables many downstream applications in 3D data processing. For a long time, RANSAC-based methods have been the gold standard for such primitive fitting problems, but they require careful per-input parameter tuning and thus do not scale well for large datasets with diverse shapes. In this work, we introduce Supervised Primitive Fitting Network (SPFN), an end-to-end neural network that can robustly detect a varying number of primitives at different scales without any user control. The network is supervised using ground truth primitive surfaces and primitive membership for the input points. Instead of directly predicting the primitives, our architecture first predicts per-point properties and then uses a differential model estimation module to compute the primitive type and parameters. We evaluate our approach on a novel benchmark of ANSI 3D mechanical component models and demonstrate a significant improvement over both the state-of-the-art RANSAC-based methods and the direct neural prediction.