pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling
Authors: Haoran You, Yu Cheng, Tianheng Cheng, ChunLiang Li, Pan Zhou
Date: 2018-11-19 00:00:00
Tasks: Image-to-Image Translation, Semantic Segmentation, Style Transfer

Recent techniques built on Generative Adversarial Networks (GANs), such as Cycle-Consistent GANs, are able to learn mappings among different domains built from unpaired datasets, through min-max optimization games between generators and discriminators. However, it remains challenging to stabilize the training process and thus cyclic models fall into mode collapse accompanied by the success of discriminator. To address this problem, we propose an novel Bayesian cyclic model and an integrated cyclic framework for inter-domain mappings. The proposed method motivated by Bayesian GAN explores the full posteriors of cyclic model via sampling latent variables and optimizes the model with maximum a posteriori (MAP) estimation. Hence, we name it Bayesian CycleGAN. In addition, original CycleGAN cannot generate diversified results. But it is feasible for Bayesian framework to diversify generated images by replacing restricted latent variables in inference process. We evaluate the proposed Bayesian CycleGAN on multiple benchmark datasets, including Cityscapes, Maps, and Monet2photo. The proposed method improve the per-pixel accuracy by 15% for the Cityscapes semantic segmentation task within origin framework and improve 20% within the proposed integrated framework, showing better resilience to imbalance confrontation. The diversified results of Monet2Photo style transfer also demonstrate its superiority over original cyclic model. We provide codes for all of our experiments in https://github.com/ranery/Bayesian-CycleGAN.
Indoor GeoNet: Weakly Supervised Hybrid Learning for Depth and Pose Estimation
Authors: Amirreza Farnoosh, Sarah Ostadabbas
Date: 2018-11-19 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation

Humans naturally perceive a 3D scene in front of them through accumulation of
information obtained from multiple interconnected projections of the scene and
by interpreting their correspondence. This phenomenon has inspired artificial
intelligence models to extract the depth and view angle of the observed scene
by modeling the correspondence between different views of that scene. Our paper
is built upon previous works in the field of unsupervised depth and relative
camera pose estimation from temporal consecutive video frames using deep
learning (DL) models. Our approach uses a hybrid learning framework introduced
in a recent work called GeoNet, which leverages geometric constraints in the 3D
scenes to synthesize a novel view from intermediate DL-based predicted depth
and relative pose. However, the state-of-the-art unsupervised depth and pose
estimation DL models are exclusively trained/tested on a few available outdoor
scene datasets and we have shown they are hardly transferable to new scenes,
especially from indoor environments, in which estimation requires higher
precision and dealing with probable occlusions. This paper introduces "Indoor
GeoNet", a weakly supervised depth and camera pose estimation model targeted
for indoor scenes. In Indoor GeoNet, we take advantage of the availability of
indoor RGBD datasets collected by human or robot navigators, and added partial
(i.e. weak) supervision in depth training into the model. Experimental results
showed that our model effectively generalizes to new scenes from different
buildings. Indoor GeoNet demonstrated significant depth and pose estimation
error reduction when compared to the original GeoNet, while showing 3 times
more reconstruction accuracy in synthesizing novel views in indoor
environments.
Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
Authors: Yunlu Xu, Chengwei Zhang, Zhanzhan Cheng, Jianwen Xie, Yi Niu, ShiLiang Pu, Fei Wu
Date: 2018-11-19 00:00:00
Tasks: Action Detection, Multiple Action Detection

This paper proposes a segregated temporal assembly recurrent (STAR) network
for weakly-supervised multiple action detection. The model learns from
untrimmed videos with only supervision of video-level labels and makes
prediction of intervals of multiple actions. Specifically, we first assemble
video clips according to class labels by an attention mechanism that learns
class-variable attention weights and thus helps the noise relieving from
background or other actions. Secondly, we build temporal relationship between
actions by feeding the assembled features into an enhanced recurrent neural
network. Finally, we transform the output of recurrent neural network into the
corresponding action distribution. In order to generate more precise temporal
proposals, we design a score term called segregated temporal gradient-weighted
class activation mapping (ST-GradCAM) fused with attention weights. Experiments
on THUMOS'14 and ActivityNet1.3 datasets show that our approach outperforms the
state-of-the-art weakly-supervised method, and performs at par with the
fully-supervised counterparts.
Transfer Learning Using Classification Layer Features of CNN
Authors: Tasfia Shermin, Manzur Murshed, Guojun Lu, Shyh Wei Teng
Date: 2018-11-19 00:00:00
Tasks: Classification, General Classification, GPU, Transfer Learning

Although CNNs have gained the ability to transfer learned knowledge from
source task to target task by virtue of large annotated datasets but consume
huge processing time to fine-tune without GPU. In this paper, we propose a new
computationally efficient transfer learning approach using classification layer
features of pre-trained CNNs by appending layer after existing classification
layer. We demonstrate that fine-tuning of the appended layer with existing
classification layer for new task converges much faster than baseline and in
average outperforms baseline classification accuracy. Furthermore, we execute
thorough experiments to examine the influence of quantity, similarity, and
dissimilarity of training sets in our classification outcomes to demonstrate
transferability of classification layer features.
Generalizable Adversarial Training via Spectral Normalization
Authors: Farzan Farnia, Jesse M. Zhang, David Tse
Date: 2018-11-19 00:00:00
Proceeding: ICLR 2019 5

Deep neural networks (DNNs) have set benchmarks on a wide array of supervised
learning tasks. Trained DNNs, however, often lack robustness to minor
adversarial perturbations to the input, which undermines their true
practicality. Recent works have increased the robustness of DNNs by fitting
networks using adversarially-perturbed training samples, but the improved
performance can still be far below the performance seen in non-adversarial
settings. A significant portion of this gap can be attributed to the decrease
in generalization performance due to adversarial training. In this work, we
extend the notion of margin loss to adversarial settings and bound the
generalization error for DNNs trained under several well-known gradient-based
attack schemes, motivating an effective regularization scheme based on spectral
normalization of the DNN's weight matrices. We also provide a
computationally-efficient method for normalizing the spectral norm of
convolutional layers with arbitrary stride and padding schemes in deep
convolutional networks. We evaluate the power of spectral normalization
extensively on combinations of datasets, network architectures, and adversarial
training schemes. The code is available at
https://github.com/jessemzhang/dl_spectral_normalization.
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
Authors: Ruijia Xu, Guanbin Li, Jihan Yang, Liang Lin
Date: 2018-11-19 00:00:00
Proceeding: ICCV 2019 10
Tasks: Domain Adaptation, Partial Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions. Previous works may not effectively uncover the underlying reasons that would lead to the drastic model degradation on the target task. In this paper, we empirically reveal that the erratic discrimination of the target domain mainly stems from its much smaller feature norms with respect to that of the source domain. To this end, we propose a novel parameter-free Adaptive Feature Norm approach. We demonstrate that progressively adapting the feature norms of the two domains to a large range of values can result in significant transfer gains, implying that those task-specific features with larger norms are more transferable. Our method successfully unifies the computation of both standard and partial domain adaptation with more robustness against the negative transfer issue. Without bells and whistles but a few lines of code, our method substantially lifts the performance on the target task and exceeds state-of-the-arts by a large margin (11.5% on Office-Home and 17.1% on VisDA2017). We hope our simple yet effective approach will shed some light on the future research of transfer learning. Code is available at https://github.com/jihanyang/AFN.
On Geometric Alignment in Low Doubling Dimension
Authors: Hu Ding, Mingquan Ye
Date: 2018-11-19 00:00:00

In real-world, many problems can be formulated as the alignment between two
geometric patterns. Previously, a great amount of research focus on the
alignment of 2D or 3D patterns, especially in the field of computer vision.
Recently, the alignment of geometric patterns in high dimension finds several
novel applications, and has attracted more and more attentions. However, the
research is still rather limited in terms of algorithms. To the best of our
knowledge, most existing approaches for high dimensional alignment are just
simple extensions of their counterparts for 2D and 3D cases, and often suffer
from the issues such as high complexities. In this paper, we propose an
effective framework to compress the high dimensional geometric patterns and
approximately preserve the alignment quality. As a consequence, existing
alignment approach can be applied to the compressed geometric patterns and thus
the time complexity is significantly reduced. Our idea is inspired by the
observation that high dimensional data often has a low intrinsic dimension. We
adopt the widely used notion "doubling dimension" to measure the extents of our
compression and the resulting approximation. Finally, we test our method on
both random and real datasets, the experimental results reveal that running the
alignment algorithm on compressed patterns can achieve similar qualities,
comparing with the results on the original patterns, but the running times
(including the times cost for compression) are substantially lower.
The PyTorch-Kaldi Speech Recognition Toolkit
Authors: Mirco Ravanelli, Titouan Parcollet, Yoshua Bengio
Date: 2018-11-19 00:00:00
Tasks: Distant Speech Recognition, Noisy Speech Recognition, Speech Recognition

The availability of open-source software is playing a remarkable role in the
popularization of speech recognition and deep learning. Kaldi, for instance, is
nowadays an established framework used to develop state-of-the-art speech
recognizers. PyTorch is used to build neural networks with the Python language
and has recently spawn tremendous interest within the machine learning
community thanks to its simplicity and flexibility.
  The PyTorch-Kaldi project aims to bridge the gap between these popular
toolkits, trying to inherit the efficiency of Kaldi and the flexibility of
PyTorch. PyTorch-Kaldi is not only a simple interface between these software,
but it embeds several useful features for developing modern speech recognizers.
For instance, the code is specifically designed to naturally plug-in
user-defined acoustic models. As an alternative, users can exploit several
pre-implemented neural networks that can be customized using intuitive
configuration files. PyTorch-Kaldi supports multiple feature and label streams
as well as combinations of neural networks, enabling the use of complex neural
architectures. The toolkit is publicly-released along with a rich documentation
and is designed to properly work locally or on HPC clusters.
  Experiments, that are conducted on several datasets and tasks, show that
PyTorch-Kaldi can effectively be used to develop modern state-of-the-art speech
recognizers.
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
Authors: Avinash Varadarajan, Pinal Bavishi, Paisan Raumviboonsuk, Peranut Chotcomwongse, Subhashini Venugopalan, Arunachalam Narayanaswamy, Jorge Cuadros, Kuniyoshi Kanai, George Bresnick, Mongkol Tadarati, Sukhum Silpa-archa, Jirawut Limwattanayingyong, Variya Nganthavee, Joe Ledsam, Pearse A. Keane, Greg S. Corrado, Lily Peng, Dale R. Webster
Date: 2018-10-18 00:00:00
Tasks: Specificity

Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites because of cost and workflow constraints. Instead, screening programs rely on the detection of hard exudates in color fundus photographs as a proxy for DME, often resulting in high false positive or false negative calls. To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME. Our model had an ROC-AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, three retinal specialists had similar sensitivities (82-85%), but only half the specificity (45-50%, p<0.001 for each comparison with model). The positive predictive value (PPV) of the model was 61% (95% CI: 56-66%), approximately double the 36-38% by the retinal specialists. In addition to predicting ci-DME, our model was able to detect the presence of intraretinal fluid with an AUC of 0.81 (95% CI: 0.81-0.86) and subretinal fluid with an AUC of 0.88 (95% CI: 0.85-0.91). The ability of deep learning algorithms to make clinically relevant predictions that generally require sophisticated 3D-imaging equipment from simple 2D images has broad relevance to many other applications in medical imaging.
Predictive and Semantic Layout Estimation for Robotic Applications in Manhattan Worlds
Authors: Armon Shariati, Bernd Pfrommer, Camillo J. Taylor
Date: 2018-11-19 00:00:00
Tasks: Motion Planning

This paper describes an approach to automatically extracting floor plans from
the kinds of incomplete measurements that could be acquired by an autonomous
mobile robot. The approach proceeds by reasoning about extended structural
layout surfaces which are automatically extracted from the available data. The
scheme can be run in an online manner to build water tight representations of
the environment. The system effectively speculates about room boundaries and
free space regions which provides useful guidance to subsequent motion planning
systems. Experimental results are presented on multiple data sets.
CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition
Authors: Nadav Schor, Oren Katzir, Hao Zhang, Daniel Cohen-Or
Date: 2018-11-19 00:00:00
Proceeding: ICCV 2019 10
Tasks: Diversity

Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks. A reoccurring challenge is how to enable a model to generate a rich variety of samples from the entire target distribution, rather than only from a distribution confined to the training data. In other words, we would like the generative model to go beyond the observed samples and learn to generate ``unseen'', yet still plausible, data. In our work, we present CompoNet, a generative neural network for 2D or 3D shapes that is based on a part-based prior, where the key idea is for the network to synthesize shapes by varying both the shape parts and their compositions. Treating a shape not as an unstructured whole, but as a (re-)composable set of deformable parts, adds a combinatorial dimension to the generative process to enrich the diversity of the output, encouraging the generator to venture more into the ``unseen''. We show that our part-based model generates richer variety of plausible shapes compared with baseline generative models. To this end, we introduce two quantitative metrics to evaluate the diversity of a generative model and assess how well the generated data covers both the training data and unseen data from the same target distribution. Code is available at https://github.com/nschor/CompoNet.
Limitations of Source-Filter Coupling In Phonation
Authors: Debasish Ray Mohapatra, Sidney Fels
Date: 2018-11-19 00:00:00

The coupling of vocal fold (source) and vocal tract (filter) is one of the
most critical factors in source-filter articulation theory. The traditional
linear source-filter theory has been challenged by current research which
clearly shows the impact of acoustic loading on the dynamic behavior of the
vocal fold vibration as well as the variations in the glottal flow pulses
shape. This paper outlines the underlying mechanism of source-filter
interactions; demonstrates the design and working principles of coupling for
the various existing vocal cord and vocal tract biomechanical models. For our
study, we have considered self-oscillating lumped-element models of the
acoustic source and computational models of the vocal tract as articulators. To
understand the limitations of source-filter interactions which are associated
with each of those models, we compare them concerning their mechanical design,
acoustic and physiological characteristics and aerodynamic simulation.
Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networks
Authors: Yuan Li, Yuanjie Yu, Zefeng Li, Yangkun Lin, Meifang Xu, Jiwei Li, Xi Zhou
Date: 2018-11-19 00:00:00
Tasks: Decoder, object-detection, Object Detection, Segmentation, Semantic Segmentation

Recently, semantic segmentation and general object detection frameworks have
been widely adopted by scene text detecting tasks. However, both of them alone
have obvious shortcomings in practice. In this paper, we propose a novel
end-to-end trainable deep neural network framework, named Pixel-Anchor, which
combines semantic segmentation and SSD in one network by feature sharing and
anchor-level attention mechanism to detect oriented scene text. To deal with
scene text which has large variances in size and aspect ratio, we combine FPN
and ASPP operation as our encoder-decoder structure in the semantic
segmentation part, and propose a novel Adaptive Predictor Layer in the SSD.
Pixel-Anchor detects scene text in a single network forward pass, no complex
post-processing other than an efficient fusion Non-Maximum Suppression is
involved. We have benchmarked the proposed Pixel-Anchor on the public datasets.
Pixel-Anchor outperforms the competing methods in terms of text localization
accuracy and run speed, more specifically, on the ICDAR 2015 dataset, the
proposed algorithm achieves an F-score of 0.8768 at 10 FPS for 960 x 1728
resolution images.
Understanding and Measuring Psychological Stress using Social Media
Authors: Sharath Chandra Guntuku, Anneke Buffone, Kokil Jaidka, Johannes Eichstaedt, Lyle Ungar
Date: 2018-11-19 00:00:00
Tasks: Domain Adaptation

A body of literature has demonstrated that users' mental health conditions,
such as depression and anxiety, can be predicted from their social media
language. There is still a gap in the scientific understanding of how
psychological stress is expressed on social media. Stress is one of the primary
underlying causes and correlates of chronic physical illnesses and mental
health conditions. In this paper, we explore the language of psychological
stress with a dataset of 601 social media users, who answered the Perceived
Stress Scale questionnaire and also consented to share their Facebook and
Twitter data. Firstly, we find that stressed users post about exhaustion,
losing control, increased self-focus and physical pain as compared to posts
about breakfast, family-time, and travel by users who are not stressed.
Secondly, we find that Facebook language is more predictive of stress than
Twitter language. Thirdly, we demonstrate how the language based models thus
developed can be adapted and be scaled to measure county-level trends. Since
county-level language is easily available on Twitter using the Streaming API,
we explore multiple domain adaptation algorithms to adapt user-level Facebook
models to Twitter language. We find that domain-adapted and scaled social
media-based measurements of stress outperform sociodemographic variables (age,
gender, race, education, and income), against ground-truth survey-based stress
measurements, both at the user- and the county-level in the U.S. Twitter
language that scores higher in stress is also predictive of poorer health, less
access to facilities and lower socioeconomic status in counties. We conclude
with a discussion of the implications of using social media as a new tool for
monitoring stress levels of both individuals and counties.
Stochastic Deep Networks
Authors: Gwendoline de Bie, Gabriel Peyré, Marco Cuturi
Date: 2018-11-19 00:00:00

Machine learning is increasingly targeting areas where input data cannot be
accurately described by a single vector, but can be modeled instead using the
more flexible concept of random vectors, namely probability measures or more
simply point clouds of varying cardinality. Using deep architectures on
measures poses, however, many challenging issues. Indeed, deep architectures
are originally designed to handle fixedlength vectors, or, using recursive
mechanisms, ordered sequences thereof. In sharp contrast, measures describe a
varying number of weighted observations with no particular order. We propose in
this work a deep framework designed to handle crucial aspects of measures,
namely permutation invariances, variations in weights and cardinality.
Architectures derived from this pipeline can (i) map measures to measures -
using the concept of push-forward operators; (ii) bridge the gap between
measures and Euclidean spaces - through integration steps. This allows to
design discriminative networks (to classify or reduce the dimensionality of
input measures), generative architectures (to synthesize measures) and
recurrent pipelines (to predict measure dynamics). We provide a theoretical
analysis of these building blocks, review our architectures' approximation
abilities and robustness w.r.t. perturbation, and try them on various
discriminative and generative tasks.
The core consistency of a compressed tensor
Authors: Georgios Tsitsikas, Evangelos E. Papalexakis
Date: 2018-11-18 00:00:00
Tasks: Tensor Decomposition

Tensor decomposition on big data has attracted significant attention
recently. Among the most popular methods is a class of algorithms that
leverages compression in order to reduce the size of the tensor and potentially
parallelize computations. A fundamental requirement for such methods to work
properly is that the low-rank tensor structure is retained upon compression. In
lieu of efficient and realistic means of computing and studying the effects of
compression on the low rank of a tensor, we study the effects of compression on
the core consistency; a widely used heuristic that has been used as a proxy for
estimating that low rank. We provide theoretical analysis, where we identify
sufficient conditions for the compression such that the core consistency is
preserved, and we conduct extensive experiments that validate our analysis.
Further, we explore popular compression schemes and how they affect the core
consistency.