pwc-archive/papers-with-abstracts huggingface dataset:
Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images
Authors: Zhongping Ji, Xiao Qi, Yigang Wang, Gang Xu, Peng Du, Qing Wu
Date: 2018-06-22 00:00:00
Tasks: Data Augmentation

3D content creation is referred to as one of the most fundamental tasks of
computer graphics. And many 3D modeling algorithms from 2D images or curves
have been developed over the past several decades. Designers are allowed to
align some conceptual images or sketch some suggestive curves, from front,
side, and top views, and then use them as references in constructing a 3D model
automatically or manually. However, to the best of our knowledge, no studies
have investigated on 3D human body reconstruction in a similar manner. In this
paper, we propose a deep learning based reconstruction of 3D human body shape
from 2D orthographic views. A novel CNN-based regression network, with two
branches corresponding to frontal and lateral views respectively, is designed
for estimating 3D human body shape from 2D mask images. We train our networks
separately to decouple the feature descriptors which encode the body parameters
from different views, and fuse them to estimate an accurate human body shape.
In addition, to overcome the shortage of training data required for this
purpose, we propose some significantly data augmentation schemes for 3D human
body shapes, which can be used to promote further research on this topic.
Extensive experimen- tal results demonstrate that visually realistic and
accurate reconstructions can be achieved effectively using our algorithm.
Requiring only binary mask images, our method can help users create their own
digital avatars quickly, and also make it easy to create digital human body for
3D game, virtual reality, online fashion shopping.
Video Inpainting by Jointly Learning Temporal Structure and Spatial Details
Authors: Chuan Wang, Haibin Huang, Xiaoguang Han, Jue Wang
Date: 2018-06-22 00:00:00
Tasks: Video Inpainting

We present a new data-driven video inpainting method for recovering missing
regions of video frames. A novel deep learning architecture is proposed which
contains two sub-networks: a temporal structure inference network and a spatial
detail recovering network. The temporal structure inference network is built
upon a 3D fully convolutional architecture: it only learns to complete a
low-resolution video volume given the expensive computational cost of 3D
convolution. The low resolution result provides temporal guidance to the
spatial detail recovering network, which performs image-based inpainting with a
2D fully convolutional network to produce recovered video frames in their
original resolution. Such two-step network design ensures both the spatial
quality of each frame and the temporal coherence across frames. Our method
jointly trains both sub-networks in an end-to-end manner. We provide
qualitative and quantitative evaluation on three datasets, demonstrating that
our method outperforms previous learning-based video inpainting methods.
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning
Authors: Xinlei Pan, Eshed Ohn-Bar, Nicholas Rhinehart, Yan Xu, Yilin Shen, Kris M. Kitani
Date: 2018-06-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Humans are able to understand and perform complex tasks by strategically
structuring the tasks into incremental steps or subgoals. For a robot
attempting to learn to perform a sequential task with critical subgoal states,
such states can provide a natural opportunity for interaction with a human
expert. This paper analyzes the benefit of incorporating a notion of subgoals
into Inverse Reinforcement Learning (IRL) with a Human-In-The-Loop (HITL)
framework. The learning process is interactive, with a human expert first
providing input in the form of full demonstrations along with some subgoal
states. These subgoal states define a set of subtasks for the learning agent to
complete in order to achieve the final goal. The learning agent queries for
partial demonstrations corresponding to each subtask as needed when the agent
struggles with the subtask. The proposed Human Interactive IRL (HI-IRL)
framework is evaluated on several discrete path-planning tasks. We demonstrate
that subgoal-based interactive structuring of the learning task results in
significantly more efficient learning, requiring only a fraction of the
demonstration data needed for learning the underlying reward function with the
baseline IRL model.
Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization
Authors: Jie Cao, Yibo Hu, Hongwen Zhang, Ran He, Zhenan Sun
Date: 2018-06-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Dictionary Learning, Face Recognition, Robust Face Recognition, Vocal Bursts Intensity Prediction

Face frontalization refers to the process of synthesizing the frontal view of
a face from a given profile. Due to self-occlusion and appearance distortion in
the wild, it is extremely challenging to recover faithful results and preserve
texture details in a high-resolution. This paper proposes a High Fidelity Pose
Invariant Model (HF-PIM) to produce photographic and identity-preserving
results. HF-PIM frontalizes the profiles through a novel texture warping
procedure and leverages a dense correspondence field to bind the 2D and 3D
surface spaces. We decompose the prerequisite of warping into dense
correspondence field estimation and facial texture map recovering, which are
both well addressed by deep networks. Different from those reconstruction
methods relying on 3D data, we also propose Adversarial Residual Dictionary
Learning (ARDL) to supervise facial texture map recovering with only monocular
images. Exhaustive experiments on both controlled and uncontrolled environments
demonstrate that the proposed method not only boosts the performance of
pose-invariant face recognition but also dramatically improves high-resolution
frontalization appearances.
Paragraph-based complex networks: application to document classification and authenticity verification
Authors: Henrique F. de Arruda, Vanessa Q. Marinho, Luciano da F. Costa, Diego R. Amancio
Date: 2018-06-22 00:00:00
Tasks: Diversity, Document Classification, General Classification, Semantic Similarity, Semantic Textual Similarity, text-classification, Text Classification

With the increasing number of texts made available on the Internet, many
applications have relied on text mining tools to tackle a diversity of
problems. A relevant model to represent texts is the so-called word adjacency
(co-occurrence) representation, which is known to capture mainly syntactical
features of texts.In this study, we introduce a novel network representation
that considers the semantic similarity between paragraphs. Two main properties
of paragraph networks are considered: (i) their ability to incorporate
characteristics that can discriminate real from artificial, shuffled
manuscripts and (ii) their ability to capture syntactical and semantic textual
features. Our results revealed that real texts are organized into communities,
which turned out to be an important feature for discriminating them from
artificial texts. Interestingly, we have also found that, differently from
traditional co-occurrence networks, the adopted representation is able to
capture semantic features. Additionally, the proposed framework was employed to
analyze the Voynich manuscript, which was found to be compatible with texts
written in natural languages. Taken together, our findings suggest that the
proposed methodology can be combined with traditional network models to improve
text classification tasks.
A Novel ECOC Algorithm with Centroid Distance Based Soft Coding Scheme
Authors: Kaijie Feng, Kun-Hong Liu, Beizhan Wang
Date: 2018-06-22 00:00:00

In ECOC framework, the ternary coding strategy is widely deployed in coding
process. It relabels classes with {"-1,0,1" }, where -1/1 means to assign the
corresponding classes to the negative/positive group, and label 0 leads to
ignore the corresponding classes in the training process. However, the
application of hard labels may lose some information about the tendency of
class distributions. Instead, we propose a Centroid distance-based Soft coding
scheme to indicate such tendency, named as CSECOC. In our algorithm, Sequential
Forward Floating Selection (SFFS) is applied to search an optimal class
assignment by minimizing the ratio of intra-group and inter-group distance. In
this way, a hard coding matrix is generated initially. Then we propose a
measure, named as coverage, to describe the probability of a sample in a class
falling to a correct group. The coverage of a class a group replace the
corresponding hard element, so as to form a soft coding matrix. Compared with
the hard ones, such soft elements can reflect the tendency of a class belonging
to positive or negative group. Instead of classifiers, regressors are used as
base learners in this algorithm. To the best of our knowledge, it is the first
time that soft coding scheme has been proposed. The results on five UCI
datasets show that compared with some state-of-art ECOC algorithms, our
algorithm can produce comparable or better classification accuracy with small
scale ensembles.
TriResNet: A Deep Triple-stream Residual Network for Histopathology Grading
Authors: Rene Bidart, Alexander Wong
Date: 2018-06-22 00:00:00

While microscopic analysis of histopathological slides is generally
considered as the gold standard method for performing cancer diagnosis and
grading, the current method for analysis is extremely time consuming and labour
intensive as it requires pathologists to visually inspect tissue samples in a
detailed fashion for the presence of cancer. As such, there has been
significant recent interest in computer aided diagnosis systems for analysing
histopathological slides for cancer grading to aid pathologists to perform
cancer diagnosis and grading in a more efficient, accurate, and consistent
manner. In this work, we investigate and explore a deep triple-stream residual
network (TriResNet) architecture for the purpose of tile-level histopathology
grading, which is the critical first step to computer-aided whole-slide
histopathology grading. In particular, the design mentality behind the proposed
TriResNet network architecture is to facilitate for the learning of a more
diverse set of quantitative features to better characterize the complex tissue
characteristics found in histopathology samples. Experimental results on two
widely-used computer-aided histopathology benchmark datasets (CAMELYON16
dataset and Invasive Ductal Carcinoma (IDC) dataset) demonstrated that the
proposed TriResNet network architecture was able to achieve noticeably improved
accuracies when compared with two other state-of-the-art deep convolutional
neural network architectures. Based on these promising results, the hope is
that the proposed TriResNet network architecture could become a useful tool to
aiding pathologists increase the consistency, speed, and accuracy of the
histopathology grading process.
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation
Authors: Hareesh Bahuleyan, Lili Mou, Hao Zhou, Olga Vechtomova
Date: 2018-06-22 00:00:00
Proceeding: NAACL 2019 6
Tasks: Sentence, Text Generation

The variational autoencoder (VAE) imposes a probabilistic distribution
(typically Gaussian) on the latent space and penalizes the Kullback--Leibler
(KL) divergence between the posterior and prior. In NLP, VAEs are extremely
difficult to train due to the problem of KL collapsing to zero. One has to
implement various heuristics such as KL weight annealing and word dropout in a
carefully engineered manner to successfully train a VAE for text. In this
paper, we propose to use the Wasserstein autoencoder (WAE) for probabilistic
sentence generation, where the encoder could be either stochastic or
deterministic. We show theoretically and empirically that, in the original WAE,
the stochastically encoded Gaussian distribution tends to become a Dirac-delta
function, and we propose a variant of WAE that encourages the stochasticity of
the encoder. Experimental results show that the latent space learned by WAE
exhibits properties of continuity and smoothness as in VAEs, while
simultaneously achieving much higher BLEU scores for sentence reconstruction.
Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies
Authors: Yan Xu, Li Xing, Jessica Su, Xuekui Zhang, Weiliang Qiu
Date: 2018-06-21 00:00:00
Tasks: Clustering

Genome-wide association studies (GWASs) aim to detect genetic risk factors
for complex human diseases by identifying disease-associated single-nucleotide
polymorphisms (SNPs). The traditional SNP-wise approach along with multiple
testing adjustment is over-conservative and lack of power in many GWASs. In
this article, we proposed a model-based clustering method that transforms the
challenging high-dimension-small-sample-size problem to
low-dimension-large-sample-size problem and borrows information across SNPs by
grouping SNPs into three clusters. We pre-specify the patterns of clusters by
minor allele frequencies of SNPs between cases and controls, and enforce the
patterns with prior distributions. In the simulation studies our proposed novel
model outperform traditional SNP-wise approach by showing better controls of
false discovery rate (FDR) and higher sensitivity. We re-analyzed two real
studies to identifying SNPs associated with severe bortezomib-induced
peripheral neuropathy (BiPN) in patients with multiple myeloma (MM). The
original analysis in the literature failed to identify SNPs after FDR
adjustment. Our proposed method not only detected the reported SNPs after FDR
adjustment but also discovered a novel BiPN-associated SNP rs4351714 that has
been reported to be related to MM in another study.
Generalizing Correspondence Analysis for Applications in Machine Learning
Authors: Hsiang Hsu, Salman Salamatian, Flavio P. Calmon
Date: 2018-06-21 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction, Epidemiology, Representation Learning

Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random variables. CA has found applications in fields ranging from epidemiology to social sciences; however, current methods do not scale to large, high-dimensional datasets. In this paper, we provide a novel interpretation of CA in terms of an information-theoretic quantity called the principal inertia components. We show that estimating the principal inertia components, which consists in solving a functional optimization problem over the space of finite variance functions of two random variable, is equivalent to performing CA. We then leverage this insight to design novel algorithms to perform CA at an unprecedented scale. Particularly, we demonstrate how the principal inertia components can be reliably approximated from data using deep neural networks. Finally, we show how these maximally correlated embeddings of pairs of random variables in CA further play a central role in several learning problems including visualization of classification boundary and training process, and underlying recent multi-view and multi-modal learning methods.
Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation
Authors: Zahra Mirikharaji, Ghassan Hamarneh
Date: 2018-06-21 00:00:00
Tasks: Lesion Segmentation, Segmentation, Semantic Segmentation, Skin Lesion Segmentation

Semantic segmentation is an important preliminary step towards automatic
medical image interpretation. Recently deep convolutional neural networks have
become the first choice for the task of pixel-wise class prediction. While
incorporating prior knowledge about the structure of target objects has proven
effective in traditional energy-based segmentation approaches, there has not
been a clear way for encoding prior knowledge into deep learning frameworks. In
this work, we propose a new loss term that encodes the star shape prior into
the loss function of an end-to-end trainable fully convolutional network (FCN)
framework. We penalize non-star shape segments in FCN prediction maps to
guarantee a global structure in segmentation results. Our experiments
demonstrate the advantage of regularizing FCN parameters by the star shape
prior and our results on the ISBI 2017 skin segmentation challenge data set
achieve the first rank in the segmentation task among $21$ participating teams.
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features
Authors: Chiori Hori, Huda Alamri, Jue Wang, Gordon Wichern, Takaaki Hori, Anoop Cherian, Tim K. Marks, Vincent Cartillier, Raphael Gontijo Lopes, Abhishek Das, Irfan Essa, Dhruv Batra, Devi Parikh
Date: 2018-06-21 00:00:00
Tasks: Question Answering, Video Description, Visual Question Answering, Visual Question Answering (VQA)

Dialog systems need to understand dynamic visual scenes in order to have
conversations with users about the objects and events around them. Scene-aware
dialog systems for real-world applications could be developed by integrating
state-of-the-art technologies from multiple research areas, including:
end-to-end dialog technologies, which generate system responses using models
trained from dialog data; visual question answering (VQA) technologies, which
answer questions about images using learned image features; and video
description technologies, in which descriptions/captions are generated from
videos using multimodal information. We introduce a new dataset of dialogs
about videos of human behaviors. Each dialog is a typed conversation that
consists of a sequence of 10 question-and-answer(QA) pairs between two Amazon
Mechanical Turk (AMT) workers. In total, we collected dialogs on roughly 9,000
videos. Using this new dataset for Audio Visual Scene-aware dialog (AVSD), we
trained an end-to-end conversation model that generates responses in a dialog
about a video. Our experiments demonstrate that using multimodal features that
were developed for multimodal attention-based video description enhances the
quality of generated dialog about dynamic scenes (videos). Our dataset, model
code and pretrained models will be publicly available for a new Video
Scene-Aware Dialog challenge.
Learning to Rank from Samples of Variable Quality
Authors: Mostafa Dehghani, Jaap Kamps
Date: 2018-06-21 00:00:00
Tasks: Document Ranking, Learning-To-Rank

Training deep neural networks requires many training samples, but in
practice, training labels are expensive to obtain and may be of varying
quality, as some may be from trusted expert labelers while others might be from
heuristics or other sources of weak supervision such as crowd-sourcing. This
creates a fundamental quality-versus quantity trade-off in the learning
process. Do we learn from the small amount of high-quality data or the
potentially large amount of weakly-labeled data? We argue that if the learner
could somehow know and take the label-quality into account when learning the
data representation, we could get the best of both worlds. To this end, we
introduce "fidelity-weighted learning" (FWL), a semi-supervised student-teacher
approach for training deep neural networks using weakly-labeled data. FWL
modulates the parameter updates to a student network (trained on the task we
care about) on a per-sample basis according to the posterior confidence of its
label-quality estimated by a teacher (who has access to the high-quality
labels). Both student and teacher are learned from the data. We evaluate FWL on
document ranking where we outperform state-of-the-art alternative
semi-supervised methods.
The Natural Language Decathlon: Multitask Learning as Question Answering
Authors: Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, Richard Socher
Date: 2018-06-20 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Domain Adaptation, Machine Translation, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Inference, Question Answering, Relation Extraction, Semantic Parsing, Semantic Role Labeling, Sentiment Analysis, Text Classification, Transfer Learning, Translation

Deep learning has improved performance on many natural language processing
(NLP) tasks individually. However, general NLP models cannot emerge within a
paradigm that focuses on the particularities of a single metric, dataset, and
task. We introduce the Natural Language Decathlon (decaNLP), a challenge that
spans ten tasks: question answering, machine translation, summarization,
natural language inference, sentiment analysis, semantic role labeling,
zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and
commonsense pronoun resolution. We cast all tasks as question answering over a
context. Furthermore, we present a new Multitask Question Answering Network
(MQAN) jointly learns all tasks in decaNLP without any task-specific modules or
parameters in the multitask setting. MQAN shows improvements in transfer
learning for machine translation and named entity recognition, domain
adaptation for sentiment analysis and natural language inference, and zero-shot
capabilities for text classification. We demonstrate that the MQAN's
multi-pointer-generator decoder is key to this success and performance further
improves with an anti-curriculum training strategy. Though designed for
decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic
parsing task in the single-task setting. We also release code for procuring and
processing data, training and evaluating models, and reproducing all
experiments for decaNLP.
Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates
Authors: Justus T. C. Schwabedal, John C. Snyder, Ayse Cakmak, Shamim Nemati, Gari. D. Clifford
Date: 2018-06-20 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification

Randomizing the Fourier-transform (FT) phases of temporal-spatial data
generates surrogates that approximate examples from the data-generating
distribution. We propose such FT surrogates as a novel tool to augment and
analyze training of neural networks and explore the approach in the example of
sleep-stage classification. By computing FT surrogates of raw EEG, EOG, and EMG
signals of under-represented sleep stages, we balanced the CAPSLPDB sleep
database. We then trained and tested a convolutional neural network for sleep
stage classification, and found that our surrogate-based augmentation improved
the mean F1-score by 7%. As another application of FT surrogates, we formulated
an approach to compute saliency maps for individual sleep epochs. The
visualization is based on the response of inferred class probabilities under
replacement of short data segments by partial surrogates. To quantify how well
the distributions of the surrogates and the original data match, we evaluated a
trained classifier on surrogates of correctly classified examples, and
summarized these conditional predictions in a confusion matrix. We show how
such conditional confusion matrices can qualitatively explain the performance
of surrogates in class balancing. The FT-surrogate augmentation approach may
improve classification on noisy signals if carefully adapted to the data
distribution under analysis.