pwc-archive/papers-with-abstracts huggingface dataset:
Domain Generalization via Conditional Invariant Representation
Authors: Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, DaCheng Tao
Date: 2018-07-23 00:00:00
Tasks: Domain Generalization

Domain generalization aims to apply knowledge gained from multiple labeled
source domains to unseen target domains. The main difficulty comes from the
dataset bias: training data and test data have different distributions, and the
training set contains heterogeneous samples from different distributions. Let
$X$ denote the features, and $Y$ be the class labels. Existing domain
generalization methods address the dataset bias problem by learning a
domain-invariant representation $h(X)$ that has the same marginal distribution
$\mathbb{P}(h(X))$ across multiple source domains. The functional relationship
encoded in $\mathbb{P}(Y|X)$ is usually assumed to be stable across domains
such that $\mathbb{P}(Y|h(X))$ is also invariant. However, it is unclear
whether this assumption holds in practical problems. In this paper, we consider
the general situation where both $\mathbb{P}(X)$ and $\mathbb{P}(Y|X)$ can
change across all domains. We propose to learn a feature representation which
has domain-invariant class conditional distributions $\mathbb{P}(h(X)|Y)$. With
the conditional invariant representation, the invariance of the joint
distribution $\mathbb{P}(h(X),Y)$ can be guaranteed if the class prior
$\mathbb{P}(Y)$ does not change across training and test domains. Extensive
experiments on both synthetic and real data demonstrate the effectiveness of
the proposed method.
DASN:Data-Aware Skilled Network for Accurate MR Brain Tissue Segmentation
Authors: Yang Deng, Yao Sun, Yongpei Zhu, Shuo Zhang, Mingwang Zhu, Kehong Yuan
Date: 2018-07-23 00:00:00
Tasks: Segmentation

Accurate segmentation of MR brain tissue is a crucial step for diagnosis,
surgical planning, and treatment of brain abnormalities. Automatic and reliable
segmenta-tion methods are required to assist doctor. Over the last few years,
deep learning especially deep convolutional neural networks (CNNs) have emerged
as one of the most prominent approaches for image recognition problems in
various do-mains. But the improvement of deep networks always needs
inspiration, which is rare for the ordinary. Until now,there have been
reasonable MR brain tissue segmentation methods,all of which can achieve
promising performance. These different methods have their own characteristic
and are distinctive for data sets. In other words, different models performance
vary widely on the same data sets and each model has what it is skilled in. It
is on the basis of this, we propose a judgement to distinguish data sets that
different models are good at. With our method, the segmentation accuracy can be
improved easily based on the existing models, neither without increasing
training data nor improving the network. We validate our method on the widely
used IBSR 18 dataset and obtain average dice ratio of 88.06%,while it is 85.82%
and 86.92% when only using separate one model respectively.
Deep attention-guided fusion network for lesion segmentation
Authors: Hengliang Zhu, Yangyang Hao, Lizhuang Ma, Ruixing Li, Hua Wang
Date: 2018-07-23 00:00:00
Tasks: Deep Attention, Lesion Segmentation, Segmentation

We participated the Task 1: Lesion Segmentation. The paper describes our
algorithm and the final result of validation set for the ISIC Challenge 2018 -
Skin Lesion Analysis Towards Melanoma Detection.
Zero-shot keyword spotting for visual speech recognition in-the-wild
Authors: Themos Stafylakis, Georgios Tzimiropoulos
Date: 2018-07-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Keyword Spotting, speech-recognition, Speech Recognition, Visual Keyword Spotting, Visual Speech Recognition

Visual keyword spotting (KWS) is the problem of estimating whether a text
query occurs in a given recording using only video information. This paper
focuses on visual KWS for words unseen during training, a real-world, practical
setting which so far has received no attention by the community. To this end,
we devise an end-to-end architecture comprising (a) a state-of-the-art visual
feature extractor based on spatiotemporal Residual Networks, (b) a
grapheme-to-phoneme model based on sequence-to-sequence neural networks, and
(c) a stack of recurrent neural networks which learn how to correlate visual
features with the keyword representation. Different to prior works on KWS,
which try to learn word representations merely from sequences of graphemes
(i.e. letters), we propose the use of a grapheme-to-phoneme encoder-decoder
model which learns how to map words to their pronunciation. We demonstrate that
our system obtains very promising visual-only KWS results on the challenging
LRS2 database, for keywords unseen during training. We also show that our
system outperforms a baseline which addresses KWS via automatic speech
recognition (ASR), while it drastically improves over other recently proposed
ASR-free KWS methods.
Multimodal Social Media Analysis for Gang Violence Prevention
Authors: Philipp Blandfort, Desmond Patton, William R. Frey, Svebor Karaman, Surabhi Bhargava, Fei-Tzin Lee, Siddharth Varia, Chris Kedzie, Michael B. Gaskell, Rossano Schifanella, Kathleen McKeown, Shih-Fu Chang
Date: 2018-07-23 00:00:00
Tasks: General Classification

Gang violence is a severe issue in major cities across the U.S. and recent
studies [Patton et al. 2017] have found evidence of social media communications
that can be linked to such violence in communities with high rates of exposure
to gang activity. In this paper we partnered computer scientists with social
work researchers, who have domain expertise in gang violence, to analyze how
public tweets with images posted by youth who mention gang associations on
Twitter can be leveraged to automatically detect psychosocial factors and
conditions that could potentially assist social workers and violence outreach
workers in prevention and early intervention programs. To this end, we
developed a rigorous methodology for collecting and annotating tweets. We
gathered 1,851 tweets and accompanying annotations related to visual concepts
and the psychosocial codes: aggression, loss, and substance use. These codes
are relevant to social work interventions, as they represent possible pathways
to violence on social media. We compare various methods for classifying tweets
into these three classes, using only the text of the tweet, only the image of
the tweet, or both modalities as input to the classifier. In particular, we
analyze the usefulness of mid-level visual concepts and the role of different
modalities for this tweet classification task. Our experiments show that
individually, text information dominates classification performance of the loss
class, while image information dominates the aggression and substance use
classes. Our multimodal approach provides a very promising improvement (18%
relative in mean average precision) over the best single modality approach.
Finally, we also illustrate the complexity of understanding social media data
and elaborate on open challenges.
2P-DNN : Privacy-Preserving Deep Neural Networks Based on Homomorphic Cryptosystem
Authors: Qiang Zhu, Xixiang Lv
Date: 2018-07-23 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving

Machine Learning as a Service (MLaaS), such as Microsoft Azure, Amazon AWS,
offers an effective DNN model to complete the machine learning task for small
businesses and individuals who are restricted to the lacking data and computing
power. However, here comes an issue that user privacy is ex-posed to the MLaaS
server, since users need to upload their sensitive data to the MLaaS server. In
order to preserve their privacy, users can encrypt their data before uploading
it. This makes it difficult to run the DNN model because it is not designed for
running in ciphertext domain. In this paper, using the Paillier homomorphic
cryptosystem we present a new Privacy-Preserving Deep Neural Network model that
we called 2P-DNN. This model can fulfill the machine leaning task in ciphertext
domain. By using 2P-DNN, MLaaS is able to provide a Privacy-Preserving machine
learning ser-vice for users. We build our 2P-DNN model based on LeNet-5, and
test it with the encrypted MNIST dataset. The classification accuracy is more
than 97%, which is close to the accuracy of LeNet-5 running with the MNIST
dataset and higher than that of other existing Privacy-Preserving machine
learning models
Learning to Play Pong using Policy Gradient Learning
Authors: Somnuk Phon-Amnuaisuk
Date: 2018-07-23 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Activities in reinforcement learning (RL) revolve around learning the Markov
decision process (MDP) model, in particular, the following parameters: state
values, V; state-action values, Q; and policy, pi. These parameters are
commonly implemented as an array. Scaling up the problem means scaling up the
size of the array and this will quickly lead to a computational bottleneck. To
get around this, the RL problem is commonly formulated to learn a specific task
using hand-crafted input features to curb the size of the array. In this
report, we discuss an alternative end-to-end Deep Reinforcement Learning (DRL)
approach where the DRL attempts to learn general task representations which in
our context refers to learning to play the Pong game from a sequence of screen
snapshots without game-specific hand-crafted features. We apply artificial
neural networks (ANN) to approximate a policy of the RL model. The policy
network, via Policy Gradients (PG) method, learns to play the Pong game from a
sequence of frames without any extra semantics apart from the pixel information
and the score. In contrast to the traditional tabular RL approach where the
contents in the array have clear interpretations such as V or Q, the
interpretation of knowledge content from the weights of the policy network is
more illusive. In this work, we experiment with various Deep ANN architectures
i.e., Feed forward ANN (FFNN), Convolution ANN (CNN) and Asynchronous Advantage
Actor-Critic (A3C). We also examine the activation of hidden nodes and the
weights between the input and the hidden layers, before and after the DRL has
successfully learnt to play the Pong game. Insights into the internal learning
mechanisms and future research directions are then discussed.
LinkNBed: Multi-Graph Representation Learning with Entity Linkage
Authors: Rakshit Trivedi, Bunyamin Sisman, Jun Ma, Christos Faloutsos, Hongyuan Zha, Xin Luna Dong
Date: 2018-07-23 00:00:00
Proceeding: ACL 2018 7
Tasks: Graph Representation Learning, Knowledge Graphs, Link Prediction, Relational Reasoning, Representation Learning

Knowledge graphs have emerged as an important model for studying complex
multi-relational data. This has given rise to the construction of numerous
large scale but incomplete knowledge graphs encoding information extracted from
various resources. An effective and scalable approach to jointly learn over
multiple graphs and eventually construct a unified graph is a crucial next step
for the success of knowledge-based inference for many downstream applications.
To this end, we propose LinkNBed, a deep relational learning framework that
learns entity and relationship representations across multiple graphs. We
identify entity linkage across graphs as a vital component to achieve our goal.
We design a novel objective that leverage entity linkage and build an efficient
multi-task training procedure. Experiments on link prediction and entity
linkage demonstrate substantial improvements over the state-of-the-art
relational learning approaches.
Aligning Points to Lines: Provable Approximations
Authors: Ibrahim Jubran, Dan Feldman
Date: 2018-07-23 00:00:00

We suggest a new optimization technique for minimizing the sum $\sum_{i=1}^n f_i(x)$ of $n$ non-convex real functions that satisfy a property that we call piecewise log-Lipschitz. This is by forging links between techniques in computational geometry, combinatorics and convex optimization. As an example application, we provide the first constant-factor approximation algorithms whose running-time is polynomial in $n$ for the fundamental problem of \emph{Points-to-Lines alignment}: Given $n$ points $p_1,\cdots,p_n$ and $n$ lines $\ell_1,\cdots,\ell_n$ on the plane and $z>0$, compute the matching $\pi:[n]\to[n]$ and alignment (rotation matrix $R$ and a translation vector $t$) that minimize the sum of Euclidean distances $\sum_{i=1}^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z$ between each point to its corresponding line. This problem is non-trivial even if $z=1$ and the matching $\pi$ is given. If $\pi$ is given, the running time of our algorithms is $O(n^3)$, and even near-linear in $n$ using core-sets that support: streaming, dynamic, and distributed parallel computations in poly-logarithmic update time. Generalizations for handling e.g. outliers or pseudo-distances such as $M$-estimators for the problem are also provided. Experimental results and open source code show that our provable algorithms improve existing heuristics also in practice. A companion demonstration video in the context of Augmented Reality shows how such algorithms may be used in real-time systems.
Question Relevance in Visual Question Answering
Authors: Prakruthi Prabhakar, Nitish Kulkarni, Linghao Zhang
Date: 2018-07-23 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Free-form and open-ended Visual Question Answering systems solve the problem
of providing an accurate natural language answer to a question pertaining to an
image. Current VQA systems do not evaluate if the posed question is relevant to
the input image and hence provide nonsensical answers when posed with
irrelevant questions to an image. In this paper, we solve the problem of
identifying the relevance of the posed question to an image. We address the
problem as two sub-problems. We first identify if the question is visual or
not. If the question is visual, we then determine if it's relevant to the image
or not. For the second problem, we generate a large dataset from existing
visual question answering datasets in order to enable the training of complex
architectures and model the relevance of a visual question to an image. We also
compare the results of our Long Short-Term Memory Recurrent Neural Network
based models to Logistic Regression, XGBoost and multi-layer perceptron based
approaches to the problem.
Actor-Action Semantic Segmentation with Region Masks
Authors: Kang Dang, Chunluan Zhou, Zhigang Tu, Michael Hoy, Justin Dauwels, Junsong Yuan
Date: 2018-07-23 00:00:00
Tasks: Action Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

In this paper, we study the actor-action semantic segmentation problem, which
requires joint labeling of both actor and action categories in video frames.
One major challenge for this task is that when an actor performs an action,
different body parts of the actor provide different types of cues for the
action category and may receive inconsistent action labeling when they are
labeled independently. To address this issue, we propose an end-to-end
region-based actor-action segmentation approach which relies on region masks
from an instance segmentation algorithm. Our main novelty is to avoid labeling
pixels in a region mask independently - instead we assign a single action label
to these pixels to achieve consistent action labeling. When a pixel belongs to
multiple region masks, max pooling is applied to resolve labeling conflicts.
Our approach uses a two-stream network as the front-end (which learns features
capturing both appearance and motion information), and uses two region-based
segmentation networks as the back-end (which takes the fused features from the
two-stream network as the input and predicts actor-action labeling).
Experiments on the A2D dataset demonstrate that both the region-based
segmentation strategy and the fused features from the two-stream network
contribute to the performance improvements. The proposed approach outperforms
the state-of-the-art results by more than 8% in mean class accuracy, and more
than 5% in mean class IOU, which validates its effectiveness.
Subsampling MCMC - An introduction for the survey statistician
Authors: Matias Quiroz, Mattias Villani, Robert Kohn, Minh-Ngoc Tran, Khue-Dung Dang
Date: 2018-07-23 00:00:00
Tasks: Survey, Survey Sampling

The rapid development of computing power and efficient Markov Chain Monte
Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics,
making it a highly practical inference method in applied work. However, MCMC
algorithms tend to be computationally demanding, and are particularly slow for
large datasets. Data subsampling has recently been suggested as a way to make
MCMC methods scalable on massively large data, utilizing efficient sampling
schemes and estimators from the survey sampling literature. These developments
tend to be unknown by many survey statisticians who traditionally work with
non-Bayesian methods, and rarely use MCMC. Our article explains the idea of
data subsampling in MCMC by reviewing one strand of work, Subsampling MCMC, a
so called pseudo-marginal MCMC approach to speeding up MCMC through data
subsampling. The review is written for a survey statistician without previous
knowledge of MCMC methods since our aim is to motivate survey sampling experts
to contribute to the growing Subsampling MCMC literature.
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
Authors: Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, Stan Z. Li
Date: 2018-07-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Pedestrian Detection

Pedestrian detection in crowded scenes is a challenging problem since the
pedestrians often gather together and occlude each other. In this paper, we
propose a new occlusion-aware R-CNN (OR-CNN) to improve the detection accuracy
in the crowd. Specifically, we design a new aggregation loss to enforce
proposals to be close and locate compactly to the corresponding objects.
Meanwhile, we use a new part occlusion-aware region of interest (PORoI) pooling
unit to replace the RoI pooling layer in order to integrate the prior structure
information of human body with visibility prediction into the network to handle
occlusion. Our detector is trained in an end-to-end fashion, which achieves
state-of-the-art results on three pedestrian detection datasets, i.e.,
CityPersons, ETH, and INRIA, and performs on-pair with the state-of-the-arts on
Caltech.
Visual Mesh: Real-time Object Detection Using Constant Sample Density
Authors: Trent Houliston, Stephan K. Chalup
Date: 2018-07-23 00:00:00
Tasks: Object, object-detection, Object Detection, Real-Time Object Detection

This paper proposes an enhancement of convolutional neural networks for
object detection in resource-constrained robotics through a geometric input
transformation called Visual Mesh. It uses object geometry to create a graph in
vision space, reducing computational complexity by normalizing the pixel and
feature density of objects. The experiments compare the Visual Mesh with
several other fast convolutional neural networks. The results demonstrate
execution times sixteen times quicker than the fastest competitor tested, while
achieving outstanding accuracy.
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation
Authors: Lei Bi, Dagan Feng, Jinman Kim
Date: 2018-07-23 00:00:00
Tasks: Data Augmentation, Generative Adversarial Network, Lesion Segmentation, Melanoma Diagnosis, Segmentation, Skin Lesion Segmentation

Segmentation of skin lesions is considered as an important step in computer
aided diagnosis (CAD) for automated melanoma diagnosis. In recent years,
segmentation methods based on fully convolutional networks (FCN) have achieved
great success in general images. This success is primarily due to the
leveraging of large labelled datasets to learn features that correspond to the
shallow appearance as well as the deep semantics of the images. However, the
dependence on large dataset does not translate well into medical images. To
improve the FCN performance for skin lesion segmentations, researchers
attempted to use specific cost functions or add post-processing algorithms to
refine the coarse boundaries of the FCN results. However, the performance of
these methods is heavily reliant on the tuning of many parameters and
post-processing techniques. In this paper, we leverage the state-of-the-art
image feature learning method of generative adversarial network (GAN) for its
inherent ability to produce consistent and realistic image features by using
deep neural networks and adversarial learning concept. We improve upon GAN such
that skin lesion features can be learned at different level of complexities, in
a controlled manner. The outputs from our method is then augmented to the
existing FCN training data, thus increasing the overall feature diversity. We
evaluated our method on the ISIC 2018 skin lesion segmentation challenge
dataset and showed that it was more accurate and robust when compared to the
existing skin lesion segmentation methods.
Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models
Authors: Balázs Csanád Csáji
Date: 2018-07-23 00:00:00

A standard model of (conditional) heteroscedasticity, i.e., the phenomenon
that the variance of a process changes over time, is the Generalized
AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is
especially important for economics and finance. GARCH models are typically
estimated by the Quasi-Maximum Likelihood (QML) method, which works under mild
statistical assumptions. Here, we suggest a finite sample approach, called
ScoPe, to construct distribution-free confidence regions around the QML
estimate, which have exact coverage probabilities, despite no additional
assumptions about moments are made. ScoPe is inspired by the recently developed
Sign-Perturbed Sums (SPS) method, which however cannot be applied in the GARCH
case. ScoPe works by perturbing the score function using randomly permuted
residuals. This produces alternative samples which lead to exact confidence
regions. Experiments on simulated and stock market data are also presented, and
ScoPe is compared with the asymptotic theory and bootstrap approaches.
Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting
Authors: Markus D. Foote, Blake E. Zimmerman, Amit Sawant, Sarang Joshi
Date: 2018-07-22 00:00:00
Tasks: Image Registration, Motion Estimation, Position

Radiation therapy presents a need for dynamic tracking of a target tumor volume. Fiducial markers such as implanted gold seeds have been used to gate radiation delivery but the markers are invasive and gating significantly increases treatment time. Pretreatment acquisition of a respiratory correlated 4DCT allows for determination of accurate motion tracking which is useful in treatment planning. We design a patient-specific motion subspace and a deep convolutional neural network to recover anatomical positions from a single fluoroscopic projection in real-time. We use this deep network to approximate the nonlinear inverse of a diffeomorphic deformation composed with radiographic projection. This network recovers subspace coordinates to define the patient-specific deformation of the lungs from a baseline anatomic position. The geometric accuracy of the subspace deformations on real patient data is similar to accuracy attained by original image registration between individual respiratory-phase image volumes.