pwc-archive/papers-with-abstracts huggingface dataset:
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples
Authors: Xiaojun Jia, Xingxing Wei, Xiaochun Cao, Hassan Foroosh
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Compression

Deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples. Specifically, adding imperceptible perturbations to clean images can fool the well trained deep neural networks. In this paper, we propose an end-to-end image compression model to defend adversarial examples: \textbf{ComDefend}. The proposed model consists of a compression convolutional neural network (ComCNN) and a reconstruction convolutional neural network (ResCNN). The ComCNN is used to maintain the structure information of the original image and purify adversarial perturbations. And the ResCNN is used to reconstruct the original image with high quality. In other words, ComDefend can transform the adversarial image to its clean version, which is then fed to the trained classifier. Our method is a pre-processing module, and does not modify the classifier's structure during the whole process. Therefore, it can be combined with other model-specific defense models to jointly improve the classifier's robustness. A series of experiments conducted on MNIST, CIFAR10 and ImageNet show that the proposed method outperforms the state-of-the-art defense methods, and is consistently effective to protect classifiers against adversarial attacks.
Instance-level Facial Attributes Transfer with Geometry-Aware Flow
Authors: Weidong Yin, Ziwei Liu, Chen Change Loy
Date: 2018-11-30 00:00:00
Tasks: Attribute, Hallucination

We address the problem of instance-level facial attribute transfer without
paired training data, e.g. faithfully transferring the exact mustache from a
source face to a target face. This is a more challenging task than the
conventional semantic-level attribute transfer, which only preserves the
generic attribute style instead of instance-level traits. We propose the use of
geometry-aware flow, which serves as a well-suited representation for modeling
the transformation between instance-level facial attributes. Specifically, we
leverage the facial landmarks as the geometric guidance to learn the
differentiable flows automatically, despite of the large pose gap existed.
Geometry-aware flow is able to warp the source face attribute into the target
face context and generate a warp-and-blend result. To compensate for the
potential appearance gap between source and target faces, we propose a
hallucination sub-network that produces an appearance residual to further
refine the warp-and-blend result. Finally, a cycle-consistency framework
consisting of both attribute transfer module and attribute removal module is
designed, so that abundant unpaired face images can be used as training data.
Extensive evaluations validate the capability of our approach in transferring
instance-level facial attributes faithfully across large pose and appearance
gaps. Thanks to the flow representation, our approach can readily be applied to
generate realistic details on high-resolution images.
Improved Crowding Distance for NSGA-II
Authors: Xiangxiang Chu, Xinjie Yu
Date: 2018-11-30 00:00:00

Non-dominated sorting genetic algorithm II (NSGA-II) does well in dealing
with multi-objective problems. When evaluating validity of an algorithm for
multi-objective problems, two kinds of indices are often considered
simultaneously, i.e. the convergence to Pareto Front and the distribution
characteristic. The crowding distance in the standard NSGA-II has the property
that solutions within a cubic have the same crowding distance, which has no
contribution to the convergence of the algorithm. Actually the closer to the
Pareto Front a solution is, the higher priority it should have. In the paper,
the crowding distance is redefined while keeping almost all the advantages of
the original one. Moreover, the speed of converging to the Pareto Front is
faster. Finally, the improvement is proved to be effective by applying it to
solve nine Benchmark problems.
FSNet: An Identity-Aware Generative Model for Image-based Face Swapping
Authors: Ryota Natsume, Tatsuya Yatagawa, Shigeo Morishima
Date: 2018-11-30 00:00:00
Tasks: Face Swapping

This paper presents FSNet, a deep generative model for image-based face
swapping. Traditionally, face-swapping methods are based on three-dimensional
morphable models (3DMMs), and facial textures are replaced between the
estimated three-dimensional (3D) geometries in two images of different
individuals. However, the estimation of 3D geometries along with different
lighting conditions using 3DMMs is still a difficult task. We herein represent
the face region with a latent variable that is assigned with the proposed deep
neural network (DNN) instead of facial textures. The proposed DNN synthesizes a
face-swapped image using the latent variable of the face region and another
image of the non-face region. The proposed method is not required to fit to the
3DMM; additionally, it performs face swapping only by feeding two face images
to the proposed network. Consequently, our DNN-based face swapping performs
better than previous approaches for challenging inputs with different face
orientations and lighting conditions. Through several experiments, we
demonstrated that the proposed method performs face swapping in a more stable
manner than the state-of-the-art method, and that its results are compatible
with the method thereof.
Classification is a Strong Baseline for Deep Metric Learning
Authors: Andrew Zhai, Hao-Yu Wu
Date: 2018-11-30 00:00:00
Tasks: Binarization, Classification, Clustering, Content-Based Image Retrieval, Face Verification, General Classification, Image Retrieval, Metric Learning, Retrieval, Triplet

Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images. Two major applications of metric learning are content-based image retrieval and face verification. For the retrieval tasks, the majority of current state-of-the-art (SOTA) approaches are triplet-based non-parametric training. For the face verification tasks, however, recent SOTA approaches have adopted classification-based parametric training. In this paper, we look into the effectiveness of classification based approaches on image retrieval datasets. We evaluate on several standard retrieval datasets such as CAR-196, CUB-200-2011, Stanford Online Product, and In-Shop datasets for image retrieval and clustering, and establish that our classification-based approach is competitive across different feature dimensions and base feature networks. We further provide insights into the performance effects of subsampling classes for scalable classification-based training, and the effects of binarization, enabling efficient storage and computation for practical applications.
AI Neurotechnology for Aging Societies -- Task-load and Dementia EEG Digital Biomarker Development Using Information Geometry Machine Learning Methods
Authors: Tomasz M. Rutkowski, Qibin Zhao, Masao S. Abe, Mihoko Otake
Date: 2018-11-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Dementia and especially Alzheimer's disease (AD) are the most common causes
of cognitive decline in elderly people. A spread of the above mentioned mental
health problems in aging societies is causing a significant medical and
economic burden in many countries around the world. According to a recent World
Health Organization (WHO) report, it is approximated that currently, worldwide,
about 47 million people live with a dementia spectrum of neurocognitive
disorders. This number is expected to triple by 2050, which calls for possible
application of AI-based technologies to support an early screening for
preventive interventions and a subsequent mental wellbeing monitoring as well
as maintenance with so-called digital-pharma or beyond a pill therapeutical
approaches. This paper discusses our attempt and preliminary results of
brainwave (EEG) techniques to develop digital biomarkers for dementia progress
detection and monitoring. We present an information geometry-based
classification approach for automatic EEG-derived event related responses
(ERPs) discrimination of low versus high task-load auditory or tactile stimuli
recognition, of which amplitude and latency variabilities are similar to those
in dementia. The discussed approach is a step forward to develop AI, and
especially machine learning (ML) approaches, for the subsequent application to
mild-cognitive impairment (MCI) and AD diagnostics.
Transferable Adversarial Attacks for Image and Video Object Detection
Authors: Xingxing Wei, Siyuan Liang, Ning Chen, Xiaochun Cao
Date: 2018-11-30 00:00:00
Tasks: Generative Adversarial Network, Object, object-detection, Object Detection, Video Object Detection

Adversarial examples have been demonstrated to threaten many computer vision tasks including object detection. However, the existing attacking methods for object detection have two limitations: poor transferability, which denotes that the generated adversarial examples have low success rate to attack other kinds of detection methods, and high computation cost, which means that they need more time to generate an adversarial image, and therefore are difficult to deal with the video data. To address these issues, we utilize a generative mechanism to obtain the adversarial image and video. In this way, the processing time is reduced. To enhance the transferability, we destroy the feature maps extracted from the feature network, which usually constitutes the basis of object detectors. The proposed method is based on the Generative Adversarial Network (GAN) framework, where we combine the high-level class loss and low-level feature loss to jointly train the adversarial example generator. A series of experiments conducted on PASCAL VOC and ImageNet VID datasets show that our method can efficiently generate image and video adversarial examples, and more importantly, these adversarial examples have better transferability, and thus, are able to simultaneously attack two kinds of representative object detection models: proposal based models like Faster-RCNN, and regression based models like SSD.
Inferring Concept Prerequisite Relations from Online Educational Resources
Authors: Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, Vaibhav Rajan
Date: 2018-11-30 00:00:00

The Internet has rich and rapidly increasing sources of high quality
educational content. Inferring prerequisite relations between educational
concepts is required for modern large-scale online educational technology
applications such as personalized recommendations and automatic curriculum
creation. We present PREREQ, a new supervised learning method for inferring
concept prerequisite relations. PREREQ is designed using latent representations
of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a
neural network based on the Siamese network architecture. PREREQ can learn
unknown concept prerequisites from course prerequisites and labeled concept
prerequisite data. It outperforms state-of-the-art approaches on benchmark
datasets and can effectively learn from very less training data. PREREQ can
also use unlabeled video playlists, a steadily growing source of training data,
to learn concept prerequisites, thus obviating the need for manual annotation
of course prerequisites.
Towards Robust Lung Segmentation in Chest Radiographs with Deep Learning
Authors: Jyoti Islam, Yanqing Zhang
Date: 2018-11-30 00:00:00
Tasks: Deep Learning, Segmentation

Automated segmentation of Lungs plays a crucial role in the computer-aided
diagnosis of chest X-Ray (CXR) images. Developing an efficient Lung
segmentation model is challenging because of difficulties such as the presence
of several edges at the rib cage and clavicle, inconsistent lung shape among
different individuals, and the appearance of the lung apex. In this paper, we
propose a robust model for Lung segmentation in Chest Radiographs. Our model
learns to ignore the irrelevant regions in an input Chest Radiograph while
highlighting regions useful for lung segmentation. The proposed model is
evaluated on two public chest X-Ray datasets (Montgomery County, MD, USA, and
Shenzhen No. 3 People's Hospital in China). The experimental result with a DICE
score of 98.6% demonstrates the robustness of our proposed lung segmentation
approach.
ADSaS: Comprehensive Real-time Anomaly Detection System
Authors: Sooyeon Lee, Huy Kang Kim
Date: 2018-11-30 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

Since with massive data growth, the need for autonomous and generic anomaly
detection system is increased. However, developing one stand-alone generic
anomaly detection system that is accurate and fast is still a challenge. In
this paper, we propose conventional time-series analysis approaches, the
Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Seasonal
Trend decomposition using Loess (STL), to detect complex and various anomalies.
Usually, SARIMA and STL are used only for stationary and periodic time-series,
but by combining, we show they can detect anomalies with high accuracy for data
that is even noisy and non-periodic. We compared the algorithm to Long Short
Term Memory (LSTM), a deep-learning-based algorithm used for anomaly detection
system. We used a total of seven real-world datasets and four artificial
datasets with different time-series properties to verify the performance of the
proposed algorithm.
LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
Authors: Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, Dianbo Liu
Date: 2018-11-30 00:00:00
Tasks: BIG-bench Machine Learning

Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. Sharing data among different sources is a big challenge due to regulatory, operational and security reasons. One potential solution is federated machine learning, which is a method that sends machine learning algorithms simultaneously to all data sources, trains models in each source and aggregates the learned models. This strategy allows utilization of valuable data without moving them. One challenge in applying federated machine learning is the possibly different distributions of data from diverse sources. To tackle this problem, we proposed an adaptive boosting method named LoAdaBoost that increases the efficiency of federated machine learning. Using intensive care unit data from hospitals, we investigated the performance of learning in IID and non-IID data distribution scenarios, and showed that the proposed LoAdaBoost method achieved higher predictive accuracy with lower computational complexity than the baseline method.
Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders
Authors: Hyungu Kahng, Yonghyun Jeong, Yoon Sang Cho, Gonie Ahn, Young Joon Park, Uk Jo, Hankyu Lee, Hyungrok Do, Junseung Lee, Hyunjin Choi, Iljoo Yoon, Hyunjae Lee, Daehun Jun, Changhyeon Bae, Seoung Bum Kim
Date: 2018-11-30 00:00:00
Tasks: Decision Making, Decoder, Real-Time Strategy Games, Starcraft

StarCraft, one of the most popular real-time strategy games, is a compelling
environment for artificial intelligence research for both micro-level unit
control and macro-level strategic decision making. In this study, we address an
eminent problem concerning macro-level decision making, known as the
'fog-of-war', which rises naturally from the fact that information regarding
the opponent's state is always provided in the incomplete form. For intelligent
agents to play like human players, it is obvious that making accurate
predictions of the opponent's status under incomplete information will increase
its chance of winning. To reflect this fact, we propose a convolutional
encoder-decoder architecture that predicts potential counts and locations of
the opponent's units based on only partially visible and noisy information. To
evaluate the performance of our proposed method, we train an additional
classifier on the encoder-decoder output to predict the game outcome (win or
lose). Finally, we designed an agent incorporating the proposed method and
conducted simulation games against rule-based agents to demonstrate both
effectiveness and practicality. All experiments were conducted on actual game
replay data acquired from professional players.
Modality-based Factorization for Multimodal Fusion
Authors: Elham J. Barezi, Peyman Momeni, Pascale Fung
Date: 2018-11-30 00:00:00
Proceeding: WS 2019 8
Tasks: Emotion Recognition, Personality Trait Recognition, Sentiment Analysis

We propose a novel method, Modality-based Redundancy Reduction Fusion (MRRF), for understanding and modulating the relative contribution of each modality in multimodal inference tasks. This is achieved by obtaining an $(M+1)$-way tensor to consider the high-order relationships between $M$ modalities and the output layer of a neural network model. Applying a modality-based tensor factorization method, which adopts different factors for different modalities, results in removing information present in a modality that can be compensated by other modalities, with respect to model outputs. This helps to understand the relative utility of information in each modality. In addition it leads to a less complicated model with less parameters and therefore could be applied as a regularizer avoiding overfitting. We have applied this method to three different multimodal datasets in sentiment analysis, personality trait recognition, and emotion recognition. We are able to recognize relationships and relative importance of different modalities in these tasks and achieves a 1\% to 4\% improvement on several evaluation measures compared to the state-of-the-art for all three tasks.
An Interpretable Model with Globally Consistent Explanations for Credit Risk
Authors: Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang
Date: 2018-11-30 00:00:00

We propose a possible solution to a public challenge posed by the Fair Isaac
Corporation (FICO), which is to provide an explainable model for credit risk
assessment. Rather than present a black box model and explain it afterwards, we
provide a globally interpretable model that is as accurate as other neural
networks. Our "two-layer additive risk model" is decomposable into subscales,
where each node in the second layer represents a meaningful subscale, and all
of the nonlinearities are transparent. We provide three types of explanations
that are simpler than, but consistent with, the global model. One of these
explanation methods involves solving a minimum set cover problem to find
high-support globally-consistent explanations. We present a new online
visualization tool to allow users to explore the global model and its
explanations.
Virtual Class Enhanced Discriminative Embedding Learning
Authors: Binghui Chen, Weihong Deng, Haifeng Shen
Date: 2018-11-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Face Verification, General Classification

Recently, learning discriminative features to improve the recognition
performances gradually becomes the primary goal of deep learning, and numerous
remarkable works have emerged. In this paper, we propose a novel yet extremely
simple method \textbf{Virtual Softmax} to enhance the discriminative property
of learned features by injecting a dynamic virtual negative class into the
original softmax. Injecting virtual class aims to enlarge inter-class margin
and compress intra-class distribution by strengthening the decision boundary
constraint. Although it seems weird to optimize with this additional virtual
class, we show that our method derives from an intuitive and clear motivation,
and it indeed encourages the features to be more compact and separable. This
paper empirically and experimentally demonstrates the superiority of Virtual
Softmax, improving the performances on a variety of object classification and
face verification tasks.
DeepFlux for Skeletons in the Wild
Authors: Yukang Wang, Yongchao Xu, Stavros Tsogkas, Xiang Bai, Sven Dickinson, Kaleem Siddiqi
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Edge Detection, Object, Object Skeleton Detection, Semantic Segmentation, Symmetry Detection

Computing object skeletons in natural images is challenging, owing to large
variations in object appearance and scale, and the complexity of handling
background clutter. Many recent methods frame object skeleton detection as a
binary pixel classification problem, which is similar in spirit to
learning-based edge detection, as well as to semantic segmentation methods. In
the present article, we depart from this strategy by training a CNN to predict
a two-dimensional vector field, which maps each scene point to a candidate
skeleton pixel, in the spirit of flux-based skeletonization algorithms. This
"image context flux" representation has two major advantages over previous
approaches. First, it explicitly encodes the relative position of skeletal
pixels to semantically meaningful entities, such as the image points in their
spatial context, and hence also the implied object boundaries. Second, since
the skeleton detection context is a region-based vector field, it is better
able to cope with object parts of large width. We evaluate the proposed method
on three benchmark datasets for skeleton detection and two for symmetry
detection, achieving consistently superior performance over state-of-the-art
methods.
Learning Dynamics from Kinematics: Estimating 2D Foot Pressure Maps from Video Frames
Authors: Christopher Funk, Savinay Nagendra, Jesse Scott, Bharadwaj Ravichandran, John H. Challis, Robert T. Collins, Yanxi Liu
Date: 2018-11-30 00:00:00

Pose stability analysis is the key to understanding locomotion and control of body equilibrium, with applications in numerous fields such as kinesiology, medicine, and robotics. In biomechanics, Center of Pressure (CoP) is used in studies of human postural control and gait. We propose and validate a novel approach to learn CoP from pose of a human body to aid stability analysis. More specifically, we propose an end-to-end deep learning architecture to regress foot pressure heatmaps, and hence the CoP locations, from 2D human pose derived from video. We have collected a set of long (5min +) choreographed Taiji (Tai Chi) sequences of multiple subjects with synchronized foot pressure and video data. The derived human pose data and corresponding foot pressure maps are used jointly in training a convolutional neural network with residual architecture, named PressNET. Cross-subject validation results show promising performance of PressNET, significantly outperforming the baseline method of K-Nearest Neighbors. Furthermore, we demonstrate that our computation of center of pressure (CoP) from PressNET is not only significantly more accurate than those obtained from the baseline approach but also meets the expectations of corresponding lab-based measurements of stability studies in kinesiology.
Adversarial Examples as an Input-Fault Tolerance Problem
Authors: Angus Galloway, Anna Golubeva, Graham W. Taylor
Date: 2018-11-30 00:00:00
Tasks: valid

We analyze the adversarial examples problem in terms of a model's fault
tolerance with respect to its input. Whereas previous work focuses on
arbitrarily strict threat models, i.e., $\epsilon$-perturbations, we consider
arbitrary valid inputs and propose an information-based characteristic for
evaluating tolerance to diverse input faults.