pwc-archive/papers-with-abstracts huggingface dataset:
How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples
Authors: Utku Ozbulak, Wesley De Neve, Arnout Van Messem
Date: 2018-11-21 00:00:00

Even before deep learning architectures became the de facto models for
complex computer vision tasks, the softmax function was, given its elegant
properties, already used to analyze the predictions of feedforward neural
networks. Nowadays, the output of the softmax function is also commonly used to
assess the strength of adversarial examples: malicious data points designed to
fail machine learning models during the testing phase. However, in this paper,
we show that it is possible to generate adversarial examples that take
advantage of some properties of the softmax function, leading to undesired
outcomes when interpreting the strength of the adversarial examples at hand.
Specifically, we argue that the output of the softmax function is a poor
indicator when the strength of an adversarial example is analyzed and that this
indicator can be easily tricked by already existing methods for adversarial
example generation.
Unsupervised Single Image Deraining with Self-supervised Constraints
Authors: Xin Jin, Zhibo Chen, Jianxin Lin, Zhikai Chen, Wei Zhou
Date: 2018-11-21 00:00:00
Tasks: Benchmarking, Generative Adversarial Network, Rain Removal, Single Image Deraining

Most existing single image deraining methods require learning supervised
models from a large set of paired synthetic training data, which limits their
generality, scalability and practicality in real-world multimedia applications.
Besides, due to lack of labeled-supervised constraints, directly applying
existing unsupervised frameworks to the image deraining task will suffer from
low-quality recovery. Therefore, we propose an Unsupervised Deraining
Generative Adversarial Network (UD-GAN) to tackle above problems by introducing
self-supervised constraints from the intrinsic statistics of unpaired rainy and
clean images. Specifically, we firstly design two collaboratively optimized
modules, namely Rain Guidance Module (RGM) and Background Guidance Module
(BGM), to take full advantage of rainy image characteristics: The RGM is
designed to discriminate real rainy images from fake rainy images which are
created based on outputs of the generator with BGM. Simultaneously, the BGM
exploits a hierarchical Gaussian-Blur gradient error to ensure background
consistency between rainy input and de-rained output. Secondly, a novel
luminance-adjusting adversarial loss is integrated into the clean image
discriminator considering the built-in luminance difference between real clean
images and derained images. Comprehensive experiment results on various
benchmarking datasets and different training settings show that UD-GAN
outperforms existing image deraining methods in both quantitative and
qualitative comparisons.
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War
Authors: Jonas Gehring, Da Ju, Vegard Mella, Daniel Gant, Nicolas Usunier, Gabriel Synnaeve
Date: 2018-11-21 00:00:00
Tasks: Real-Time Strategy Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Vocal Bursts Intensity Prediction

We consider the problem of high-level strategy selection in the adversarial
setting of real-time strategy games from a reinforcement learning perspective,
where taking an action corresponds to switching to the respective strategy.
Here, a good strategy successfully counters the opponent's current and possible
future strategies which can only be estimated using partial observations. We
investigate whether we can utilize the full game state information during
training time (in the form of an auxiliary prediction task) to increase
performance. Experiments carried out within a StarCraft: Brood War bot against
strong community bots show substantial win rate improvements over a
fixed-strategy baseline and encouraging results when learning with the
auxiliary task.
Adaptive Re-ranking of Deep Feature for Person Re-identification
Authors: Yong Liu, Lin Shang, Andy Song
Date: 2018-11-21 00:00:00
Tasks: Person Re-Identification, Re-Ranking, Retrieval

Typical person re-identification (re-ID) methods train a deep CNN to extract
deep features and combine them with a distance metric for the final evaluation.
In this work, we focus on exploiting the full information encoded in the deep
feature to boost the re-ID performance. First, we propose a Deep Feature Fusion
(DFF) method to exploit the diverse information embedded in a deep feature. DFF
treats each sub-feature as an information carrier and employs a diffusion
process to exchange their information. Second, we propose an Adaptive
Re-Ranking (ARR) method to exploit the contextual information encoded in the
features of neighbors. ARR utilizes the contextual information to re-rank the
retrieval results in an iterative manner. Particularly, it adds more contextual
information after each iteration automatically to consider more matches. Third,
we propose a strategy that combines DFF and ARR to enhance the performance.
Extensive comparative evaluations demonstrate the superiority of the proposed
methods on three large benchmarks.
Adjustable Real-time Style Transfer
Authors: Mohammad Babaeizadeh, Golnaz Ghiasi
Date: 2018-11-21 00:00:00
Proceeding: ICLR 2020 1
Tasks: Style Transfer

Artistic style transfer is the problem of synthesizing an image with content
similar to a given image and style similar to another. Although recent
feed-forward neural networks can generate stylized images in real-time, these
models produce a single stylization given a pair of style/content images, and
the user doesn't have control over the synthesized output. Moreover, the style
transfer depends on the hyper-parameters of the model with varying "optimum"
for different input images. Therefore, if the stylized output is not appealing
to the user, she/he has to try multiple models or retrain one with different
hyper-parameters to get a favorite stylization. In this paper, we address these
issues by proposing a novel method which allows adjustment of crucial
hyper-parameters, after the training and in real-time, through a set of
manually adjustable parameters. These parameters enable the user to modify the
synthesized outputs from the same pair of style/content images, in search of a
favorite stylized image. Our quantitative and qualitative experiments indicate
how adjusting these parameters is comparable to retraining the model with
different hyper-parameters. We also demonstrate how these parameters can be
randomized to generate results which are diverse but still very similar in
style and content.
Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural Networks
Authors: Jennifer N. Wei, David Belanger, Ryan P. Adams, D. Sculley
Date: 2018-11-21 00:00:00
Tasks: BIG-bench Machine Learning

When confronted with a substance of unknown identity, researchers often
perform mass spectrometry on the sample and compare the observed spectrum to a
library of previously-collected spectra to identify the molecule. While
popular, this approach will fail to identify molecules that are not in the
existing library. In response, we propose to improve the library's coverage by
augmenting it with synthetic spectra that are predicted using machine learning.
We contribute a lightweight neural network model that quickly predicts mass
spectra for small molecules. Achieving high accuracy predictions requires a
novel neural network architecture that is designed to capture typical
fragmentation patterns from electron ionization. We analyze the effects of our
modeling innovations on library matching performance and compare our models to
prior machine learning-based work on spectrum prediction.
Neural Machine Translation with Adequacy-Oriented Learning
Authors: Xiang Kong, Zhaopeng Tu, Shuming Shi, Eduard Hovy, Tong Zhang
Date: 2018-11-21 00:00:00
Tasks: Attribute, Machine Translation, NMT, Reinforcement Learning, Reinforcement Learning (RL), Translation

Although Neural Machine Translation (NMT) models have advanced
state-of-the-art performance in machine translation, they face problems like
the inadequate translation. We attribute this to that the standard Maximum
Likelihood Estimation (MLE) cannot judge the real translation quality due to
its several limitations. In this work, we propose an adequacy-oriented learning
mechanism for NMT by casting translation as a stochastic policy in
Reinforcement Learning (RL), where the reward is estimated by explicitly
measuring translation adequacy. Benefiting from the sequence-level training of
RL strategy and a more accurate reward designed specifically for translation,
our model outperforms multiple strong baselines, including (1) standard and
coverage-augmented attention models with MLE-based training, and (2) advanced
reinforcement and adversarial training strategies with rewards based on both
word-level BLEU and character-level chrF3. Quantitative and qualitative
analyses on different language pairs and NMT architectures demonstrate the
effectiveness and universality of the proposed approach.
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
Authors: Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Date: 2018-11-21 00:00:00
Tasks: model, Model-based Reinforcement Learning, Reinforcement Learning

We study the sample complexity of model-based reinforcement learning (henceforth RL) in general contextual decision processes that require strategic exploration to find a near-optimal policy. We design new algorithms for RL with a generic model class and analyze their statistical properties. Our algorithms have sample complexity governed by a new structural parameter called the witness rank, which we show to be small in several settings of interest, including factored MDPs. We also show that the witness rank is never larger than the recently proposed Bellman rank parameter governing the sample complexity of the model-free algorithm OLIVE (Jiang et al., 2017), the only other provably sample-efficient algorithm for global exploration at this level of generality. Focusing on the special case of factored MDPs, we prove an exponential lower bound for a general class of model-free approaches, including OLIVE, which, when combined with our algorithmic results, demonstrates exponential separation between model-based and model-free RL in some rich-observation settings.
Seeing in the dark with recurrent convolutional neural networks
Authors: Till S. Hartmann
Date: 2018-11-21 00:00:00
Tasks: Autonomous Driving

Classical convolutional neural networks (cCNNs) are very good at categorizing
objects in images. But, unlike human vision which is relatively robust to noise
in images, the performance of cCNNs declines quickly as image quality worsens.
Here we propose to use recurrent connections within the convolutional layers to
make networks robust against pixel noise such as could arise from imaging at
low light levels, and thereby significantly increase their performance when
tested with simulated noisy video sequences. We show that cCNNs classify images
with high signal to noise ratios (SNRs) well, but are easily outperformed when
tested with low SNR images (high noise levels) by convolutional neural networks
that have recurrency added to convolutional layers, henceforth referred to as
gruCNNs. Addition of Bayes-optimal temporal integration to allow the cCNN to
integrate multiple image frames still does not match gruCNN performance.
Additionally, we show that at low SNRs, the probabilities predicted by the
gruCNN (after calibration) have higher confidence than those predicted by the
cCNN. We propose to consider recurrent connections in the early stages of
neural networks as a solution to computer vision under imperfect lighting
conditions and noisy environments; challenges faced during real-time video
streams of autonomous driving at night, during rain or snow, and other
non-ideal situations.
Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides
Authors: Naofumi Tomita, Behnaz Abdollahi, Jason Wei, Bing Ren, Arief Suriawinata, Saeed Hassanpour
Date: 2018-11-20 00:00:00
Tasks: Crop Classification, Diagnostic, General Classification, image-classification, Image Classification, Medical Object Detection

Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results. These approaches, however, require a laborious annotation process and are fragmented. This diagnostic study collected deidentified high-resolution histological images (N = 379) for training a new model composed of a convolutional neural network and a grid-based attention network, trainable without region-of-interest annotations. Histological images of patients who underwent endoscopic esophagus and gastroesophageal junction mucosal biopsy between January 1, 2016, and December 31, 2018, at Dartmouth-Hitchcock Medical Center (Lebanon, New Hampshire) were collected. The method achieved a mean accuracy of 0.83 in classifying 123 test images. These results were comparable with or better than the performance from the current state-of-the-art sliding window approach, which was trained with regions of interest. Results of this study suggest that the proposed attention-based deep neural network framework for Barrett esophagus and esophageal adenocarcinoma detection is important because it is based solely on tissue-level annotations, unlike existing methods that are based on regions of interest. This new model is expected to open avenues for applying deep learning to digital pathology.
Joint association and classification analysis of multi-view data
Authors: Yunfeng Zhang, Irina Gaynanova
Date: 2018-11-20 00:00:00
Tasks: Classification, General Classification

Multi-view data, that is matched sets of measurements on the same subjects, have become increasingly common with advances in multi-omics technology. Often, it is of interest to find associations between the views that are related to the intrinsic class memberships. Existing association methods cannot directly incorporate class information, while existing classification methods do not take into account between-views associations. In this work, we propose a framework for Joint Association and Classification Analysis of multi-view data (JACA). Our goal is not to merely improve the misclassification rates, but to provide a latent representation of high-dimensional data that is both relevant for the subtype discrimination and coherent across the views. We motivate the methodology by establishing a connection between canonical correlation analysis and discriminant analysis. We also establish the estimation consistency of JACA in high-dimensional settings. A distinct advantage of JACA is that it can be applied to the multi-view data with block-missing structure, that is to cases where a subset of views or class labels is missing for some subjects. The application of JACA to quantify the associations between RNAseq and miRNA views with respect to consensus molecular subtypes in colorectal cancer data from The Cancer Genome Atlas project leads to improved misclassification rates and stronger found associations compared to existing methods.
An Empirical Assessment of the Complexity and Realism of Synthetic Social Contact Networks
Authors: Kiran Karra, Samarth Swarup, Justus Graham
Date: 2018-10-06 00:00:00

We use multiple measures of graph complexity to evaluate the realism of
synthetically-generated networks of human activity, in comparison with several
stylized network models as well as a collection of empirical networks from the
literature. The synthetic networks are generated by integrating data about
human populations from several sources, including the Census, transportation
surveys, and geographical data. The resulting networks represent an
approximation of daily or weekly human interaction. Our results indicate that
the synthetically generated graphs according to our methodology are closer to
the real world graphs, as measured across multiple structural measures, than a
range of stylized graphs generated using common network models from the
literature.
A Proposal-Based Solution to Spatio-Temporal Action Detection in Untrimmed Videos
Authors: Joshua Gleason, Rajeev Ranjan, Steven Schwarcz, Carlos D. Castillo, Jun-Chen Cheng, Rama Chellappa
Date: 2018-11-20 00:00:00
Tasks: Action Classification, Action Detection, Clustering, object-detection, Object Detection

Existing approaches for spatio-temporal action detection in videos are
limited by the spatial extent and temporal duration of the actions. In this
paper, we present a modular system for spatio-temporal action detection in
untrimmed security videos. We propose a two stage approach. The first stage
generates dense spatio-temporal proposals using hierarchical clustering and
temporal jittering techniques on frame-wise object detections. The second stage
is a Temporal Refinement I3D (TRI-3D) network that performs action
classification and temporal refinement on the generated proposals. The object
detection-based proposal generation step helps in detecting actions occurring
in a small spatial region of a video frame, while temporal jittering and
refinement helps in detecting actions of variable lengths. Experimental results
on the spatio-temporal action detection dataset - DIVA - show the effectiveness
of our system. For comparison, the performance of our system is also evaluated
on the THUMOS14 temporal action detection dataset.
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?
Authors: Tiago S. Nazare, Rodrigo F. de Mello, Moacir A. Ponti
Date: 2018-11-20 00:00:00
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, image-classification, Image Classification

Recently, several techniques have been explored to detect unusual behaviour
in surveillance videos. Nevertheless, few studies leverage features from
pre-trained CNNs and none of then present a comparison of features generate by
different models. Motivated by this gap, we compare features extracted by four
state-of-the-art image classification networks as a way of describing patches
from security video frames. We carry out experiments on the Ped1 and Ped2
datasets and analyze the usage of different feature normalization techniques.
Our results indicate that choosing the appropriate normalization is crucial to
improve the anomaly detection performance when working with CNN features. Also,
in the Ped2 dataset our approach was able to obtain results comparable to the
ones of several state-of-the-art methods. Lastly, as our method only considers
the appearance of each frame, we believe that it can be combined with
approaches that focus on motion patterns to further improve performance.
Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations
Authors: Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, Vicente Ordonez
Date: 2018-11-20 00:00:00
Proceeding: ICCV 2019 10
Tasks: Temporal Action Localization

In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables --such as gender-- in visual recognition tasks. We show that trained models significantly amplify the association of target labels with gender beyond what one would expect from biased datasets. Surprisingly, we show that even when datasets are balanced such that each label co-occurs equally with each gender, learned models amplify the association between labels and gender, as much as if data had not been balanced! To mitigate this, we adopt an adversarial approach to remove unwanted features corresponding to protected variables from intermediate representations in a deep neural network -- and provide a detailed analysis of its effectiveness. Experiments on two datasets: the COCO dataset (objects), and the imSitu dataset (actions), show reductions in gender bias amplification while maintaining most of the accuracy of the original models.
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Authors: Felipe Oviedo, Zekun Ren, Shijing Sun, Charlie Settens, Zhe Liu, Noor Titan Putri Hartono, Ramasamy Savitha, Brian L. DeCost, Siyu I. P. Tian, Giuseppe Romano, Aaron Gilad Kusne, Tonio Buonassisi
Date: 2018-11-20 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, General Classification, Space group classification, X-Ray Diffraction (XRD)

X-ray diffraction (XRD) data acquisition and analysis is among the most
time-consuming steps in the development cycle of novel thin-film materials. We
propose a machine-learning-enabled approach to predict crystallographic
dimensionality and space group from a limited number of thin-film XRD patterns.
We overcome the scarce-data problem intrinsic to novel materials development by
coupling a supervised machine learning approach with a model agnostic,
physics-informed data augmentation strategy using simulated data from the
Inorganic Crystal Structure Database (ICSD) and experimental data. As a test
case, 115 thin-film metal halides spanning 3 dimensionalities and 7
space-groups are synthesized and classified. After testing various algorithms,
we develop and implement an all convolutional neural network, with cross
validated accuracies for dimensionality and space-group classification of 93%
and 89%, respectively. We propose average class activation maps, computed from
a global average pooling layer, to allow high model interpretability by human
experimentalists, elucidating the root causes of misclassification. Finally, we
systematically evaluate the maximum XRD pattern step size (data acquisition
rate) before loss of predictive accuracy occurs, and determine it to be
0.16{\deg}, which enables an XRD pattern to be obtained and classified in 5.5
minutes or less.
MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense
Authors: Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer
Date: 2018-11-20 00:00:00
Tasks: Adversarial Defense

Solving inverse problems continues to be a central challenge in computer
vision. Existing techniques either explicitly construct an inverse mapping
using prior knowledge about the corruption, or learn the inverse directly using
a large collection of examples. However, in practice, the nature of corruption
may be unknown, and thus it is challenging to regularize the problem of
inferring a plausible solution. On the other hand, collecting task-specific
training data is tedious for known corruptions and impossible for unknown ones.
We present MimicGAN, an unsupervised technique to solve general inverse
problems based on image priors in the form of generative adversarial networks
(GANs). Using a GAN prior, we show that one can reliably recover solutions to
underdetermined inverse problems through a surrogate network that learns to
mimic the corruption at test time. Our system successively estimates the
corruption and the clean image without the need for supervisory training, while
outperforming existing baselines in blind image recovery. We also demonstrate
that MimicGAN improves upon recent GAN-based defenses against adversarial
attacks and represents one of the strongest test-time defenses available today.