pwc-archive/papers-with-abstracts huggingface dataset:
The Limitations of Adversarial Training and the Blind-Spot Attack
Authors: Huan Zhang, Hongge Chen, Zhao Song, Duane Boning, Inderjit S. Dhillon, Cho-Jui Hsieh
Date: 2019-01-15 00:00:00
Proceeding: ICLR 2019 5
Tasks: valid

The adversarial training procedure proposed by Madry et al. (2018) is one of
the most effective methods to defend against adversarial examples in deep
neural networks (DNNs). In our paper, we shed some lights on the practicality
and the hardness of adversarial training by showing that the effectiveness
(robustness on test set) of adversarial training has a strong correlation with
the distance between a test point and the manifold of training data embedded by
the network. Test examples that are relatively far away from this manifold are
more likely to be vulnerable to adversarial attacks. Consequentially, an
adversarial training based defense is susceptible to a new class of attacks,
the "blind-spot attack", where the input images reside in "blind-spots" (low
density regions) of the empirical distribution of training data but is still on
the ground-truth data manifold. For MNIST, we found that these blind-spots can
be easily found by simply scaling and shifting image pixel values. Most
importantly, for large datasets with high dimensional and complex data manifold
(CIFAR, ImageNet, etc), the existence of blind-spots in adversarial training
makes defending on any valid test examples difficult due to the curse of
dimensionality and the scarcity of training data. Additionally, we find that
blind-spots also exist on provable defenses including (Wong & Kolter, 2018) and
(Sinha et al., 2018) because these trainable robustness certificates can only
be practically optimized on a limited set of training data.
Online Algorithm for Unsupervised Sensor Selection
Authors: Arun Verma, Manjesh K. Hanawal, Csaba Szepesvári, Venkatesh Saligrama
Date: 2019-01-15 00:00:00

In many security and healthcare systems, the detection and diagnosis systems
use a sequence of sensors/tests. Each test outputs a prediction of the latent
state and carries an inherent cost. However, the correctness of the predictions
cannot be evaluated since the ground truth annotations may not be available.
Our objective is to learn strategies for selecting a test that gives the best
trade-off between accuracy and costs in such Unsupervised Sensor Selection
(USS) problems. Clearly, learning is feasible only if ground truth can be
inferred (explicitly or implicitly) from the problem structure. It is observed
that this happens if the problem satisfies the 'Weak Dominance' (WD) property.
We set up the USS problem as a stochastic partial monitoring problem and
develop an algorithm with sub-linear regret under the WD property. We argue
that our algorithm is optimal and evaluate its performance on problem instances
generated from synthetic and real-world datasets.
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning
Authors: Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-wei H. Lehman, Andrew Ross, Aldo Faisal, Finale Doshi-Velez
Date: 2019-01-15 00:00:00
Tasks: Deep Reinforcement Learning, Mixture-of-Experts, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Sepsis is the leading cause of mortality in the ICU. It is challenging to
manage because individual patients respond differently to treatment. Thus,
tailoring treatment to the individual patient is essential for the best
outcomes. In this paper, we take steps toward this goal by applying a
mixture-of-experts framework to personalize sepsis treatment. The mixture model
selectively alternates between neighbor-based (kernel) and deep reinforcement
learning (DRL) experts depending on patient's current history. On a large
retrospective cohort, this mixture-based approach outperforms physician, kernel
only, and DRL-only experts.
Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions
Authors: Zhaoqiang Xia, Xiaopeng Hong, Xingyu Gao, Xiaoyi Feng, Guoying Zhao
Date: 2019-01-15 00:00:00
Tasks: Data Augmentation, Micro Expression Recognition, Micro-Expression Recognition

Recently, the recognition task of spontaneous facial micro-expressions has
attracted much attention with its various real-world applications. Plenty of
handcrafted or learned features have been employed for a variety of classifiers
and achieved promising performances for recognizing micro-expressions. However,
the micro-expression recognition is still challenging due to the subtle
spatiotemporal changes of micro-expressions. To exploit the merits of deep
learning, we propose a novel deep recurrent convolutional networks based
micro-expression recognition approach, capturing the spatial-temporal
deformations of micro-expression sequence. Specifically, the proposed deep
model is constituted of several recurrent convolutional layers for extracting
visual features and a classificatory layer for recognition. It is optimized by
an end-to-end manner and obviates manual feature design. To handle sequential
data, we exploit two types of extending the connectivity of convolutional
networks across temporal domain, in which the spatiotemporal deformations are
modeled in views of facial appearance and geometry separately. Besides, to
overcome the shortcomings of limited and imbalanced training samples, temporal
data augmentation strategies as well as a balanced loss are jointly used for
our deep network. By performing the experiments on three spontaneous
micro-expression datasets, we verify the effectiveness of our proposed
micro-expression recognition approach compared to the state-of-the-art methods.
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis
Authors: Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
Date: 2019-01-15 00:00:00
Proceeding: ICML 2020 1

The notion of flat minima has played a key role in the generalization studies
of deep learning models. However, existing definitions of the flatness are
known to be sensitive to the rescaling of parameters. The issue suggests that
the previous definitions of the flatness might not be a good measure of
generalization, because generalization is invariant to such rescalings. In this
paper, from the PAC-Bayesian perspective, we scrutinize the discussion
concerning the flat minima and introduce the notion of normalized flat minima,
which is free from the known scale dependence issues. Additionally, we
highlight the scale dependence of existing matrix-norm based generalization
error bounds similar to the existing flat minima definitions. Our modified
notion of the flatness does not suffer from the insufficiency, either,
suggesting it might provide better hierarchy in the hypothesis class.
SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells
Authors: Vignesh Sankar, Devinder Kumar, David A. Clausi, Graham W. Taylor, Alexander Wong
Date: 2019-01-15 00:00:00
Tasks: Computed Tomography (CT), Decision Making, Prediction

Objective: Lung cancer is the leading cause of cancer-related death
worldwide. Computer-aided diagnosis (CAD) systems have shown significant
promise in recent years for facilitating the effective detection and
classification of abnormal lung nodules in computed tomography (CT) scans.
While hand-engineered radiomic features have been traditionally used for lung
cancer prediction, there have been significant recent successes achieving
state-of-the-art results in the area of discovery radiomics. Here, radiomic
sequencers comprising of highly discriminative radiomic features are discovered
directly from archival medical data. However, the interpretation of predictions
made using such radiomic sequencers remains a challenge. Method: A novel
end-to-end interpretable discovery radiomics-driven lung cancer prediction
pipeline has been designed, build, and tested. The radiomic sequencer being
discovered possesses a deep architecture comprised of stacked interpretable
sequencing cells (SISC). Results: The SISC architecture is shown to outperform
previous approaches while providing more insight in to its decision making
process. Conclusion: The SISC radiomic sequencer is able to achieve
state-of-the-art results in lung cancer prediction, and also offers prediction
interpretability in the form of critical response maps. Significance: The
critical response maps are useful for not only validating the predictions of
the proposed SISC radiomic sequencer, but also provide improved
radiologist-machine collaboration for effective diagnosis.
Resampling detection of recompressed images via dual-stream convolutional neural network
Authors: Gang Cao, Antao Zhou, Xianglin Huang, Gege Song, Lifang Yang, Yonggui Zhu
Date: 2019-01-15 00:00:00
Tasks: parameter estimation

Resampling detection plays an important role in identifying image tampering, such as image splicing. Currently, the resampling detection is still difficult in recompressed images, which are yielded by applying resampling followed by post-JPEG compression to primary JPEG images. Except for the scenario of low quality primary compression, it remains rather challenging due to the widespread use of middle/high quality compression in imaging devices. In this paper, we propose a new convolution neural network (CNN) method to learn the resampling trace features directly from the recompressed images. To this end, a noise extraction layer based on low-order high pass filters is deployed to yield the image residual domain, which is more beneficial to extract manipulation trace features. A dual-stream CNN is presented to capture the resampling trails along different directions, where the horizontal and vertical streams are interleaved and concatenated. Lastly, the learned features are fed into Sigmoid/Softmax layer, which acts as a binary/multiple classifier for achieving the blind detection and parameter estimation of resampling, respectively. Extensive experimental results demonstrate that our proposed method could detect resampling effectively in recompressed images and outperform the state-of-the-art detectors.
Comparing Knowledge-based Reinforcement Learning to Neural Networks in a Strategy Game
Authors: Liudmyla Nechepurenko, Viktor Voss, Vyacheslav Gritsenko
Date: 2019-01-15 00:00:00
Tasks: Game of Go, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The paper reports on an experiment, in which a Knowledge-Based Reinforcement Learning (KB-RL) method was compared to a Neural Network (NN) approach in solving a classical Artificial Intelligence (AI) task. In contrast to NNs, which require a substantial amount of data to learn a good policy, the KB-RL method seeks to encode human knowledge into the solution, considerably reducing the amount of data needed for a good policy. By means of Reinforcement Learning (RL), KB-RL learns to optimize the model and improves the output of the system. Furthermore, KB-RL offers the advantage of a clear explanation of the taken decisions as well as transparent reasoning behind the solution. The goal of the reported experiment was to examine the performance of the KB-RL method in contrast to the Neural Network and to explore the capabilities of KB-RL to deliver a strong solution for the AI tasks. The results show that, within the designed settings, KB-RL outperformed the NN, and was able to learn a better policy from the available amount of data. These results support the opinion that Artificial Intelligence can benefit from the discovery and study of alternative approaches, potentially extending the frontiers of AI.
Multi-modal Ensemble Classification for Generalized Zero Shot Learning
Authors: Rafael Felix, Michele Sasdelli, Ian Reid, Gustavo Carneiro
Date: 2019-01-15 00:00:00
Tasks: Bayesian Inference, Classification, General Classification, Generalized Zero-Shot Learning, Zero-Shot Learning

Generalized zero shot learning (GZSL) is defined by a training process
containing a set of visual samples from seen classes and a set of semantic
samples from seen and unseen classes, while the testing process consists of the
classification of visual samples from seen and unseen classes. Current
approaches are based on testing processes that focus on only one of the
modalities (visual or semantic), even when the training uses both modalities
(mostly for regularizing the training process). This under-utilization of
modalities, particularly during testing, can hinder the classification accuracy
of the method. In addition, we note a scarce attention to the development of
learning methods that explicitly optimize a balanced performance of seen and
unseen classes. Such issue is one of the reasons behind the vastly superior
classification accuracy of seen classes in GZSL methods. In this paper, we
mitigate these issues by proposing a new GZSL method based on multi-modal
training and testing processes, where the optimization explicitly promotes a
balanced classification accuracy between seen and unseen classes. Furthermore,
we explore Bayesian inference for the visual and semantic classifiers, which is
another novelty of our work in the GZSL framework. Experiments show that our
method holds the state of the art (SOTA) results in terms of harmonic mean
(H-mean) classification between seen and unseen classes and area under the seen
and unseen curve (AUSUC) on several public GZSL benchmarks.
Fast and Robust Dynamic Hand Gesture Recognition via Key Frames Extraction and Feature Fusion
Authors: Hao Tang, Hong Liu, Wei Xiao, Nicu Sebe
Date: 2019-01-15 00:00:00
Tasks: Clustering, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Gesture recognition is a hot topic in computer vision and pattern
recognition, which plays a vitally important role in natural human-computer
interface. Although great progress has been made recently, fast and robust hand
gesture recognition remains an open problem, since the existing methods have
not well balanced the performance and the efficiency simultaneously. To bridge
it, this work combines image entropy and density clustering to exploit the key
frames from hand gesture video for further feature extraction, which can
improve the efficiency of recognition. Moreover, a feature fusion strategy is
also proposed to further improve feature representation, which elevates the
performance of recognition. To validate our approach in a "wild" environment,
we also introduce two new datasets called HandGesture and Action3D datasets.
Experiments consistently demonstrate that our strategy achieves competitive
results on Northwestern University, Cambridge, HandGesture and Action3D hand
gesture datasets. Our code and datasets will release at
https://github.com/Ha0Tang/HandGestureRecognition.
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection
Authors: Timo Kohlberger, Yun Liu, Melissa Moran, Po-Hsuan, Chen, Trissia Brown, Craig H. Mermel, Jason D. Hipp, Martin C. Stumpe
Date: 2019-01-15 00:00:00
Tasks: whole slide images

Digital pathology enables remote access or consults and powerful image
analysis algorithms. However, the slide digitization process can create
artifacts such as out-of-focus (OOF). OOF is often only detected upon careful
review, potentially causing rescanning and workflow delays. Although scan-time
operator screening for whole-slide OOF is feasible, manual screening for OOF
affecting only parts of a slide is impractical. We developed a convolutional
neural network (ConvFocus) to exhaustively localize and quantify the severity
of OOF regions on digitized slides. ConvFocus was developed using our refined
semi-synthetic OOF data generation process, and evaluated using real
whole-slide images spanning 3 different tissue types and 3 different stain
types that were digitized by two different scanners. ConvFocus's predictions
were compared with pathologist-annotated focus quality grades across 514
distinct regions representing 37,700 35x35 $\mu$m image patches, and 21
digitized "z-stack" whole-slide images that contain known OOF patterns. When
compared to pathologist-graded focus quality, ConvFocus achieved Spearman rank
coefficients of 0.81 and 0.94 on two scanners, and reproduced the expected OOF
patterns from z-stack scanning. We also evaluated the impact of OOF on the
accuracy of a state-of-the-art metastatic breast cancer detector and saw a
consistent decrease in performance with increasing OOF. Comprehensive
whole-slide OOF categorization could enable rescans prior to pathologist
review, potentially reducing the impact of digitization focus issues on the
clinical workflow. We show that the algorithm trained on our semi-synthetic OOF
data generalizes well to real OOF regions across tissue types, stains, and
scanners. Finally, quantitative OOF maps can flag regions that might otherwise
be misclassified by image analysis algorithms, preventing OOF-induced errors.
Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation
Authors: Zhengwei Wang, Graham Healy, Alan F. Smeaton, Tomas E. Ward
Date: 2019-01-15 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Image Retrieval, regression

Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the
application of cortically coupled computer vision to rapid image search. In
RSVP, images are presented to participants in a rapid serial sequence which can
evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram
(EEG). The contemporary approach to this problem involves supervised spatial
filtering techniques which are applied for the purposes of enhancing the
discriminative information in the EEG data. In this paper we make two primary
contributions to that field: 1) We propose a novel spatial filtering method
which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we
provide a comprehensive comparison of nine spatial filtering pipelines using
three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern
(CSP) and three linear classification methods Linear Discriminant Analysis
(LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three
pipelines without spatial filtering are used as baseline comparison. The Area
Under Curve (AUC) is used as an evaluation metric in this paper. The results
reveal that MTWLB and xDAWN spatial filtering techniques enhance the
classification performance of the pipeline but CSP does not. The results also
support the conclusion that LR can be effective for RSVP based BCI if
discriminative features are available.
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning
Authors: Ameer Haj-Ali, Qijing Huang, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek
Date: 2019-01-15 00:00:00
Tasks: Deep Reinforcement Learning, High-Level Synthesis, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The performance of the code generated by a compiler depends on the order in
which the optimization passes are applied. In high-level synthesis, the quality
of the generated circuit relates directly to the code generated by the
front-end compiler. Choosing a good order--often referred to as the
phase-ordering problem--is an NP-hard problem. In this paper, we evaluate a new
technique to address the phase-ordering problem: deep reinforcement learning.
We implement a framework in the context of the LLVM compiler to optimize the
ordering for HLS programs and compare the performance of deep reinforcement
learning to state-of-the-art algorithms that address the phase-ordering
problem. Overall, our framework runs one to two orders of magnitude faster than
these algorithms, and achieves a 16% improvement in circuit performance over
the -O3 compiler flag.
Tightening Mutual Information Based Bounds on Generalization Error
Authors: Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli
Date: 2019-01-15 00:00:00

An information-theoretic upper bound on the generalization error of supervised learning algorithms is derived. The bound is constructed in terms of the mutual information between each individual training sample and the output of the learning algorithm. The bound is derived under more general conditions on the loss function than in existing studies; nevertheless, it provides a tighter characterization of the generalization error. Examples of learning algorithms are provided to demonstrate the the tightness of the bound, and to show that it has a broad range of applicability. Application to noisy and iterative algorithms, e.g., stochastic gradient Langevin dynamics (SGLD), is also studied, where the constructed bound provides a tighter characterization of the generalization error than existing results. Finally, it is demonstrated that, unlike existing bounds, which are difficult to compute and evaluate empirically, the proposed bound can be estimated easily in practice.
Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Authors: Hao Tang, Dan Xu, Wei Wang, Yan Yan, Nicu Sebe
Date: 2019-01-14 00:00:00
Tasks: Generative Adversarial Network, Image-to-Image Translation, Translation

State-of-the-art methods for image-to-image translation with Generative
Adversarial Networks (GANs) can learn a mapping from one domain to another
domain using unpaired image data. However, these methods require the training
of one specific model for every pair of image domains, which limits the
scalability in dealing with more than two image domains. In addition, the
training stage of these methods has the common problem of model collapse that
degrades the quality of the generated images. To tackle these issues, we
propose a Dual Generator Generative Adversarial Network (G$^2$GAN), which is a
robust and scalable approach allowing to perform unpaired image-to-image
translation for multiple domains using only dual generators within a single
model. Moreover, we explore different optimization losses for better training
of G$^2$GAN, and thus make unpaired image-to-image translation with higher
consistency and better stability. Extensive experiments on six publicly
available datasets with different scenarios, i.e., architectural buildings,
seasons, landscape and human faces, demonstrate that the proposed G$^2$GAN
achieves superior model capacity and better generation performance comparing
with existing image-to-image translation GAN models.
AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data
Authors: Liheng Zhang, Guo-Jun Qi, Liqiang Wang, Jiebo Luo
Date: 2019-01-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Representation Learning

The success of deep neural networks often relies on a large amount of labeled
examples, which can be difficult to obtain in many real scenarios. To address
this challenge, unsupervised methods are strongly preferred for training neural
networks without using any labeled data. In this paper, we present a novel
paradigm of unsupervised representation learning by Auto-Encoding
Transformation (AET) in contrast to the conventional Auto-Encoding Data (AED)
approach. Given a randomly sampled transformation, AET seeks to predict it
merely from the encoded features as accurately as possible at the output end.
The idea is the following: as long as the unsupervised features successfully
encode the essential information about the visual structures of original and
transformed images, the transformation can be well predicted. We will show that
this AET paradigm allows us to instantiate a large variety of transformations,
from parameterized, to non-parameterized and GAN-induced ones. Our experiments
show that AET greatly improves over existing unsupervised approaches, setting
new state-of-the-art performances being greatly closer to the upper bounds by
their fully supervised counterparts on CIFAR-10, ImageNet and Places datasets.