pwc-archive/papers-with-abstracts huggingface dataset:
Understanding and Enhancing the Transferability of Adversarial Examples
Authors: Lei Wu, Zhanxing Zhu, Cheng Tai, Weinan E
Date: 2018-02-27 00:00:00

State-of-the-art deep neural networks are known to be vulnerable to
adversarial examples, formed by applying small but malicious perturbations to
the original inputs. Moreover, the perturbations can \textit{transfer across
models}: adversarial examples generated for a specific model will often mislead
other unseen models. Consequently the adversary can leverage it to attack
deployed systems without any query, which severely hinder the application of
deep learning, especially in the areas where security is crucial. In this work,
we systematically study how two classes of factors that might influence the
transferability of adversarial examples. One is about model-specific factors,
including network architecture, model capacity and test accuracy. The other is
the local smoothness of loss function for constructing adversarial examples.
Based on these understanding, a simple but effective strategy is proposed to
enhance transferability. We call it variance-reduced attack, since it utilizes
the variance-reduced gradient to generate adversarial example. The
effectiveness is confirmed by a variety of experiments on both CIFAR-10 and
ImageNet datasets.
Boosting Cooperative Coevolution for Large Scale Optimization with a Fine-Grained Computation Resource Allocation Strategy
Authors: Zhigang Ren, Yongsheng Liang, Aimin Zhang, Yang Yang, Zuren Feng, Lin Wang
Date: 2018-02-27 00:00:00

Cooperative coevolution (CC) has shown great potential in solving large scale
optimization problems (LSOPs). However, traditional CC algorithms often waste
part of computation resource (CR) as they equally allocate CR among all the
subproblems. The recently developed contribution-based CC (CBCC) algorithms
improve the traditional ones to a certain extent by adaptively allocating CR
according to some heuristic rules. Different from existing works, this study
explicitly constructs a mathematical model for the CR allocation (CRA) problem
in CC and proposes a novel fine-grained CRA (FCRA) strategy by fully
considering both the theoretically optimal solution of the CRA model and the
evolution characteristics of CC. FCRA takes a single iteration as a basic CRA
unit and always selects the subproblem which is most likely to make the largest
contribution to the total fitness improvement to undergo a new iteration, where
the contribution of a subproblem at a new iteration is estimated according to
its current contribution, current evolution status as well as the estimation
for its current contribution. We verified the efficiency of FCRA by combining
it with SHADE which is an excellent differential evolution variant but has
never been employed in the CC framework. Experimental results on two benchmark
suites for LSOPs demonstrate that FCRA significantly outperforms existing CRA
strategies and the resultant CC algorithm is highly competitive in solving
LSOPs.
Robust GANs against Dishonest Adversaries
Authors: Zhi Xu, Chengtao Li, Stefanie Jegelka
Date: 2018-02-27 00:00:00

Robustness of deep learning models is a property that has recently gained increasing attention. We explore a notion of robustness for generative adversarial models that is pertinent to their internal interactive structure, and show that, perhaps surprisingly, the GAN in its original form is not robust. Our notion of robustness relies on a perturbed discriminator, or noisy, adversarial interference with its feedback. We explore, theoretically and empirically, the effect of model and training properties on this robustness. In particular, we show theoretical conditions for robustness that are supported by empirical evidence. We also test the effect of regularization. Our results suggest variations of GANs that are indeed more robust to noisy attacks and have more stable training behavior, requiring less regularization in general. Inspired by our theoretical results, we further extend our framework to obtain a class of models related to WGAN, with good empirical performance. Overall, our results suggest a new perspective on understanding and designing GAN models from the viewpoint of their internal robustness.
Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning
Authors: David Isele, Reza Rahimi, Akansel Cosgun, Kaushik Subramanian, Kikuo Fujimura
Date: 2017-05-02 00:00:00
Tasks: Autonomous Vehicles, Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Providing an efficient strategy to navigate safely through unsignaled
intersections is a difficult task that requires determining the intent of other
drivers. We explore the effectiveness of Deep Reinforcement Learning to handle
intersection problems. Using recent advances in Deep RL, we are able to learn
policies that surpass the performance of a commonly-used heuristic approach in
several metrics including task completion time and goal success rate and have
limited ability to generalize. We then explore a system's ability to learn
active sensing behaviors to enable navigating safely in the case of occlusions.
Our analysis, provides insight into the intersection handling problem, the
solutions learned by the network point out several shortcomings of current
rule-based methods, and the failures of our current deep reinforcement learning
system point to future research directions.
Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification
Authors: Mingwen Dong
Date: 2018-02-27 00:00:00
Tasks: Classification, General Classification, Genre classification, Music Genre Classification

Music genre classification is one example of content-based analysis of music
signals. Traditionally, human-engineered features were used to automatize this
task and 61% accuracy has been achieved in the 10-genre classification.
However, it's still below the 70% accuracy that humans could achieve in the
same task. Here, we propose a new method that combines knowledge of human
perception study in music genre classification and the neurophysiology of the
auditory system. The method works by training a simple convolutional neural
network (CNN) to classify a short segment of the music signal. Then, the genre
of a music is determined by splitting it into short segments and then combining
CNN's predictions from all short segments. After training, this method achieves
human-level (70%) accuracy and the filters learned in the CNN resemble the
spectrotemporal receptive field (STRF) in the auditory system.
Patch Correspondences for Interpreting Pixel-level CNNs
Authors: Victor Fragoso, Chunhui Liu, Aayush Bansal, Deva Ramanan
Date: 2017-11-29 00:00:00
Tasks: Image Generation, Image-to-Image Translation, Segmentation, Semantic Segmentation, Translation

We present compositional nearest neighbors (CompNN), a simple approach to
visually interpreting distributed representations learned by a convolutional
neural network (CNN) for pixel-level tasks (e.g., image synthesis and
segmentation). It does so by reconstructing both a CNN's input and output image
by copy-pasting corresponding patches from the training set with similar
feature embeddings. To do so efficiently, it makes of a patch-match-based
algorithm that exploits the fact that the patch representations learned by a
CNN for pixel level tasks vary smoothly. Finally, we show that CompNN can be
used to establish semantic correspondences between two images and control
properties of the output image by modifying the images contained in the
training set. We present qualitative and quantitative experiments for semantic
segmentation and image-to-image translation that demonstrate that CompNN is a
good tool for interpreting the embeddings learned by pixel-level CNNs.
On Extended Long Short-term Memory and Dependent Bidirectional Recurrent Neural Network
Authors: Yuanhang Su, C. -C. Jay Kuo
Date: 2018-02-27 00:00:00
Tasks: Dependency Parsing

In this work, we first analyze the memory behavior in three recurrent neural networks (RNN) cells; namely, the simple RNN (SRN), the long short-term memory (LSTM) and the gated recurrent unit (GRU), where the memory is defined as a function that maps previous elements in a sequence to the current output. Our study shows that all three of them suffer rapid memory decay. Then, to alleviate this effect, we introduce trainable scaling factors that act like an attention mechanism to adjust memory decay adaptively. The new design is called the extended LSTM (ELSTM). Finally, to design a system that is robust to previous erroneous predictions, we propose a dependent bidirectional recurrent neural network (DBRNN). Extensive experiments are conducted on different language tasks to demonstrate the superiority of the proposed ELSTM and DBRNN solutions. The ELTSM has achieved up to 30% increase in the labeled attachment score (LAS) as compared to LSTM and GRU in the dependency parsing (DP) task. Our models also outperform other state-of-the-art models such as bi-attention and convolutional sequence to sequence (convseq2seq) by close to 10% in the LAS. The code is released as an open source (https://github.com/yuanhangsu/ELSTM-DBRNN)
Multi-View Image Generation from a Single-View
Authors: Bo Zhao, Xiao Wu, Zhi-Qi Cheng, Hao liu, Zequn Jie, Jiashi Feng
Date: 2017-04-17 00:00:00
Tasks: Image Generation, Variational Inference

This paper addresses a challenging problem -- how to generate multi-view
cloth images from only a single view input. To generate realistic-looking
images with different views from the input, we propose a new image generation
model termed VariGANs that combines the strengths of the variational inference
and the Generative Adversarial Networks (GANs). Our proposed VariGANs model
generates the target image in a coarse-to-fine manner instead of a single pass
which suffers from severe artifacts. It first performs variational inference to
model global appearance of the object (e.g., shape and color) and produce a
coarse image with a different view. Conditioned on the generated low resolution
images, it then proceeds to perform adversarial learning to fill details and
generate images of consistent details with the input. Extensive experiments
conducted on two clothing datasets, MVC and DeepFashion, have demonstrated that
images of a novel view generated by our model are more plausible than those
generated by existing approaches, in terms of more consistent global appearance
as well as richer and sharper details.
Link Prediction Based on Graph Neural Networks
Authors: Muhan Zhang, Yixin Chen
Date: 2018-02-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Graph Neural Network, Link Prediction, Prediction

Link prediction is a key problem for network-structured data. Link prediction
heuristics use some score functions, such as common neighbors and Katz index,
to measure the likelihood of links. They have obtained wide practical uses due
to their simplicity, interpretability, and for some of them, scalability.
However, every heuristic has a strong assumption on when two nodes are likely
to link, which limits their effectiveness on networks where these assumptions
fail. In this regard, a more reasonable way should be learning a suitable
heuristic from a given network instead of using predefined ones. By extracting
a local subgraph around each target link, we aim to learn a function mapping
the subgraph patterns to link existence, thus automatically learning a
`heuristic' that suits the current network. In this paper, we study this
heuristic learning paradigm for link prediction. First, we develop a novel
$\gamma$-decaying heuristic theory. The theory unifies a wide range of
heuristics in a single framework, and proves that all these heuristics can be
well approximated from local subgraphs. Our results show that local subgraphs
reserve rich information related to link existence. Second, based on the
$\gamma$-decaying theory, we propose a new algorithm to learn heuristics from
local subgraphs using a graph neural network (GNN). Its experimental results
show unprecedented performance, working consistently well on a wide range of
problems.
A High GOPs/Slice Time Series Classifier for Portable and Embedded Biomedical Applications
Authors: Hamid Soleimani, Aliasghar, Makhlooghpour, Wilten Nicola, Claudia Clopath, Emmanuel. M. Drakakis
Date: 2018-02-27 00:00:00
Tasks: Time Series, Time Series Analysis

Nowadays a diverse range of physiological data can be captured continuously
for various applications in particular wellbeing and healthcare. Such data
require efficient methods for classification and analysis. Deep learning
algorithms have shown remarkable potential regarding such analyses, however,
the use of these algorithms on low-power wearable devices is challenged by
resource constraints such as area and power consumption. Most of the available
on-chip deep learning processors contain complex and dense hardware
architectures in order to achieve the highest possible throughput. Such a trend
in hardware design may not be efficient in applications where on-node
computation is required and the focus is more on the area and power efficiency
as in the case of portable and embedded biomedical devices. This paper presents
an efficient time-series classifier capable of automatically detecting
effective features and classifying the input signals in real-time. In the
proposed classifier, throughput is traded off with hardware complexity and cost
using resource sharing techniques. A Convolutional Neural Network (CNN) is
employed to extract input features and then a Long-Short-Term-Memory (LSTM)
architecture with ternary weight precision classifies the input signals
according to the extracted features. Hardware implementation on a Xilinx FPGA
confirm that the proposed hardware can accurately classify multiple complex
biomedical time series data with low area and power consumption and outperform
all previously presented state-of-the-art records. Most notably, our classifier
reaches 1.3$\times$ higher GOPs/Slice than similar state of the art FPGA-based
accelerators.
Manifold learning with bi-stochastic kernels
Authors: Nicholas F. Marshall, Ronald R. Coifman
Date: 2017-11-17 00:00:00

In this paper we answer the following question: what is the infinitesimal
generator of the diffusion process defined by a kernel that is normalized such
that it is bi-stochastic with respect to a specified measure? More precisely,
under the assumption that data is sampled from a Riemannian manifold we
determine how the resulting infinitesimal generator depends on the potentially
nonuniform distribution of the sample points, and the specified measure for the
bi-stochastic normalization. In a special case, we demonstrate a connection to
the heat kernel. We consider both the case where only a single data set is
given, and the case where a data set and a reference set are given. The
spectral theory of the constructed operators is studied, and Nystr\"om
extension formulas for the gradients of the eigenfunctions are computed.
Applications to discrete point sets and manifold learning are discussed.
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Authors: Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Başar
Date: 2018-02-23 00:00:00
Proceeding: ICML 2018 7
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the problem of \emph{fully decentralized} multi-agent
reinforcement learning (MARL), where the agents are located at the nodes of a
time-varying communication network. Specifically, we assume that the reward
functions of the agents might correspond to different tasks, and are only known
to the corresponding agent. Moreover, each agent makes individual decisions
based on both the information observed locally and the messages received from
its neighbors over the network. Within this setting, the collective goal of the
agents is to maximize the globally averaged return over the network through
exchanging information with their neighbors. To this end, we propose two
decentralized actor-critic algorithms with function approximation, which are
applicable to large-scale MARL problems where both the number of states and the
number of agents are massively large. Under the decentralized structure, the
actor step is performed individually by each agent with no need to infer the
policies of others. For the critic step, we propose a consensus update via
communication over the network. Our algorithms are fully incremental and can be
implemented in an online fashion. Convergence analyses of the algorithms are
provided when the value functions are approximated within the class of linear
functions. Extensive simulation results with both linear and nonlinear function
approximations are presented to validate the proposed algorithms. Our work
appears to be the first study of fully decentralized MARL algorithms for
networked agents with function approximation, with provable convergence
guarantees.
Instance Embedding Transfer to Unsupervised Video Object Segmentation
Authors: Siyang Li, Bryan Seybold, Alexey Vorobyov, Alireza Fathi, Qin Huang, C. -C. Jay Kuo
Date: 2018-01-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, Optical Flow Estimation, Segmentation, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

We propose a method for unsupervised video object segmentation by
transferring the knowledge encapsulated in image-based instance embedding
networks. The instance embedding network produces an embedding vector for each
pixel that enables identifying all pixels belonging to the same object. Though
trained on static images, the instance embeddings are stable over consecutive
video frames, which allows us to link objects together over time. Thus, we
adapt the instance networks trained on static images to video object
segmentation and incorporate the embeddings with objectness and optical flow
features, without model retraining or online fine-tuning. The proposed method
outperforms state-of-the-art unsupervised segmentation methods in the DAVIS
dataset and the FBMS dataset.
Teaching Autonomous Driving Using a Modular and Integrated Approach
Authors: Jie Tang, Shaoshan Liu, Songwen Pei, Stephane Zuckerman, Chen Liu, Weisong Shi, Jean-Luc Gaudiot
Date: 2018-02-22 00:00:00
Tasks: Autonomous Driving

Autonomous driving is not one single technology but rather a complex system
integrating many technologies, which means that teaching autonomous driving is
a challenging task. Indeed, most existing autonomous driving classes focus on
one of the technologies involved. This not only fails to provide a
comprehensive coverage, but also sets a high entry barrier for students with
different technology backgrounds. In this paper, we present a modular,
integrated approach to teaching autonomous driving. Specifically, we organize
the technologies used in autonomous driving into modules. This is described in
the textbook we have developed as well as a series of multimedia online
lectures designed to provide technical overview for each module. Then, once the
students have understood these modules, the experimental platforms for
integration we have developed allow the students to fully understand how the
modules interact with each other. To verify this teaching approach, we present
three case studies: an introductory class on autonomous driving for students
with only a basic technology background; a new session in an existing embedded
systems class to demonstrate how embedded system technologies can be applied to
autonomous driving; and an industry professional training session to quickly
bring up experienced engineers to work in autonomous driving. The results show
that students can maintain a high interest level and make great progress by
starting with familiar concepts before moving onto other modules.
Multi-Observation Regression
Authors: Rafael Frongillo, Nishant A. Mehta, Tom Morgan, Bo Waggoner
Date: 2018-02-27 00:00:00
Tasks: regression

Recent work introduced loss functions which measure the error of a prediction
based on multiple simultaneous observations or outcomes. In this paper, we
explore the theoretical and practical questions that arise when using such
multi-observation losses for regression on data sets of $(x,y)$ pairs. When a
loss depends on only one observation, the average empirical loss decomposes by
applying the loss to each pair, but for the multi-observation case, empirical
loss is not even well-defined, and the possibility of statistical guarantees is
unclear without several $(x,y)$ pairs with exactly the same $x$ value. We
propose four algorithms formalizing the concept of empirical risk minimization
for this problem, two of which have statistical guarantees in settings allowing
both slow and fast convergence rates, but which are out-performed empirically
by the other two. Empirical results demonstrate practicality of these
algorithms in low-dimensional settings, while lower bounds demonstrate
intrinsic difficulty in higher dimensions. Finally, we demonstrate the
potential benefit of the algorithms over natural baselines that use traditional
single-observation losses via both lower bounds and simulations.
A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation
Authors: Junting Zhang, Chen Liang, C. -C. Jay Kuo
Date: 2017-11-10 00:00:00
Tasks: Domain Adaptation, Scene Segmentation

A domain adaptation method for urban scene segmentation is proposed in this
work. We develop a fully convolutional tri-branch network, where two branches
assign pseudo labels to images in the unlabeled target domain while the third
branch is trained with supervision based on images in the pseudo-labeled target
domain. The re-labeling and re-training processes alternate. With this design,
the tri-branch network learns target-specific discriminative representations
progressively and, as a result, the cross-domain capability of the segmenter
improves. We evaluate the proposed network on large-scale domain adaptation
experiments using both synthetic (GTA) and real (Cityscapes) images. It is
shown that our solution achieves the state-of-the-art performance and it
outperforms previous methods by a significant margin.