pwc-archive/papers-with-abstracts huggingface dataset:
RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification
Authors: Niloofar Safi Samghabadi, Deepthi Mave, Sudipta Kar, Thamar Solorio
Date: 2018-07-31 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification

This paper presents our system for "TRAC 2018 Shared Task on Aggression
Identification". Our best systems for the English dataset use a combination of
lexical and semantic features. However, for Hindi data using only lexical
features gave us the best results. We obtained weighted F1- measures of 0.5921
for the English Facebook task (ranked 12th), 0.5663 for the English Social
Media task (ranked 6th), 0.6292 for the Hindi Facebook task (ranked 1st), and
0.4853 for the Hindi Social Media task (ranked 2nd).
Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond
Authors: Risheng Liu, Yi He, Shichao Cheng, Xin Fan, Zhongxuan Luo
Date: 2018-07-31 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring

Blind image deblurring plays a very important role in many vision and
multimedia applications. Most existing works tend to introduce complex priors
to estimate the sharp image structures for blur kernel estimation. However, it
has been verified that directly optimizing these models is challenging and easy
to fall into degenerate solutions. Although several experience-based heuristic
inference strategies, including trained networks and designed iterations, have
been developed, it is still hard to obtain theoretically guaranteed accurate
solutions. In this work, a collaborative learning framework is established to
address the above issues. Specifically, we first design two modules, named
Generator and Corrector, to extract the intrinsic image structures from the
data-driven and knowledge-based perspectives, respectively. By introducing a
collaborative methodology to cascade these modules, we can strictly prove the
convergence of our image propagations to a deblurring-related optimal solution.
As a nontrivial byproduct, we also apply the proposed method to address other
related tasks, such as image interpolation and edge-preserved smoothing. Plenty
of experiments demonstrate that our method can outperform the state-of-the-art
approaches on both synthetic and real datasets.
SegStereo: Exploiting Semantic Information for Disparity Estimation
Authors: Guorun Yang, Hengshuang Zhao, Jianping Shi, Zhidong Deng, Jiaya Jia
Date: 2018-07-31 00:00:00
Proceeding: ECCV 2018 9
Tasks: Disparity Estimation, Prediction, Semantic Segmentation

Disparity estimation for binocular stereo images finds a wide range of
applications. Traditional algorithms may fail on featureless regions, which
could be handled by high-level clues such as semantic segments. In this paper,
we suggest that appropriate incorporation of semantic cues can greatly rectify
prediction in commonly-used disparity estimation frameworks. Our method
conducts semantic feature embedding and regularizes semantic cues as the loss
term to improve learning disparity. Our unified model SegStereo employs
semantic features from segmentation and introduces semantic softmax loss, which
helps improve the prediction accuracy of disparity maps. The semantic cues work
well in both unsupervised and supervised manners. SegStereo achieves
state-of-the-art results on KITTI Stereo benchmark and produces decent
prediction on both CityScapes and FlyingThings3D datasets.
Rank and Rate: Multi-task Learning for Recommender Systems
Authors: Guy Hadash, Oren Sar Shalom, Rita Osadchy
Date: 2018-07-31 00:00:00
Tasks: Multi-Task Learning, Prediction, Recommendation Systems

The two main tasks in the Recommender Systems domain are the ranking and
rating prediction tasks. The rating prediction task aims at predicting to what
extent a user would like any given item, which would enable to recommend the
items with the highest predicted scores. The ranking task on the other hand
directly aims at recommending the most valuable items for the user. Several
previous approaches proposed learning user and item representations to optimize
both tasks simultaneously in a multi-task framework. In this work we propose a
novel multi-task framework that exploits the fact that a user does a two-phase
decision process - first decides to interact with an item (ranking task) and
only afterward to rate it (rating prediction task). We evaluated our framework
on two benchmark datasets, on two different configurations and showed its
superiority over state-of-the-art methods.
Multimodal Deep Domain Adaptation
Authors: Silvia Bucci, Mohammad Reza Loghmani, Barbara Caputo
Date: 2018-07-31 00:00:00
Tasks: Domain Adaptation, Object

Typically a classifier trained on a given dataset (source domain) does not
performs well if it is tested on data acquired in a different setting (target
domain). This is the problem that domain adaptation (DA) tries to overcome and,
while it is a well explored topic in computer vision, it is largely ignored in
robotic vision where usually visual classification methods are trained and
tested in the same domain. Robots should be able to deal with unknown
environments, recognize objects and use them in the correct way, so it is
important to explore the domain adaptation scenario also in this context. The
goal of the project is to define a benchmark and a protocol for multi-modal
domain adaptation that is valuable for the robot vision community. With this
purpose some of the state-of-the-art DA methods are selected: Deep Adaptation
Network (DAN), Domain Adversarial Training of Neural Network (DANN), Automatic
Domain Alignment Layers (AutoDIAL) and Adversarial Discriminative Domain
Adaptation (ADDA). Evaluations have been done using different data types: RGB
only, depth only and RGB-D over the following datasets, designed for the
robotic community: RGB-D Object Dataset (ROD), Web Object Dataset (WOD),
Autonomous Robot Indoor Dataset (ARID), Big Berkeley Instance Recognition
Dataset (BigBIRD) and Active Vision Dataset. Although progresses have been made
on the formulation of effective adaptation algorithms and more realistic object
datasets are available, the results obtained show that, training a sufficiently
good object classifier, especially in the domain adaptation scenario, is still
an unsolved problem. Also the best way to combine depth with RGB informations
to improve the performance is a point that needs to be investigated more.
Spectrum concentration in deep residual learning: a free probability approach
Authors: Zenan Ling, Xing He, Robert C. Qiu
Date: 2018-07-31 00:00:00

We revisit the initialization of deep residual networks (ResNets) by
introducing a novel analytical tool in free probability to the community of
deep learning. This tool deals with non-Hermitian random matrices, rather than
their conventional Hermitian counterparts in the literature. As a consequence,
this new tool enables us to evaluate the singular value spectrum of the
input-output Jacobian of a fully-connected deep ResNet for both linear and
nonlinear cases. With the powerful tool of free probability, we conduct an
asymptotic analysis of the spectrum on the single-layer case, and then extend
this analysis to the multi-layer case of an arbitrary number of layers. In
particular, we propose to rescale the classical random initialization by the
number of residual units, so that the spectrum has the order of $O(1)$, when
compared with the large width and depth of the network. We empirically
demonstrate that the proposed initialization scheme learns at a speed of orders
of magnitudes faster than the classical ones, and thus attests a strong
practical relevance of this investigation.
Neural Article Pair Modeling for Wikipedia Sub-article Matching
Authors: Muhao Chen, Changping Meng, Gang Huang, Carlo Zaniolo
Date: 2018-07-31 00:00:00
Tasks: Articles, Knowledge Base Construction

Nowadays, editors tend to separate different subtopics of a long Wiki-pedia
article into multiple sub-articles. This separation seeks to improve human
readability. However, it also has a deleterious effect on many Wikipedia-based
tasks that rely on the article-as-concept assumption, which requires each
entity (or concept) to be described solely by one article. This underlying
assumption significantly simplifies knowledge representation and extraction,
and it is vital to many existing technologies such as automated knowledge base
construction, cross-lingual knowledge alignment, semantic search and data
lineage of Wikipedia entities. In this paper we provide an approach to match
the scattered sub-articles back to their corresponding main-articles, with the
intent of facilitating automated Wikipedia curation and processing. The
proposed model adopts a hierarchical learning structure that combines multiple
variants of neural document pair encoders with a comprehensive set of explicit
features. A large crowdsourced dataset is created to support the evaluation and
feature extraction for the task. Based on the large dataset, the proposed model
achieves promising results of cross-validation and significantly outperforms
previous approaches. Large-scale serving on the entire English Wikipedia also
proves the practicability and scalability of the proposed model by effectively
extracting a vast collection of newly paired main and sub-articles.
Deep Cross Modal Learning for Caricature Verification and Identification(CaVINet)
Authors: Jatin Garg, Skand Vishwanath Peri, Himanshu Tolani, Narayanan C. Krishnan
Date: 2018-07-31 00:00:00
Tasks: Caricature, Face Recognition, Face Verification, Transfer Learning

Learning from different modalities is a challenging task. In this paper, we
look at the challenging problem of cross modal face verification and
recognition between caricature and visual image modalities. Caricature have
exaggerations of facial features of a person. Due to the significant variations
in the caricatures, building vision models for recognizing and verifying data
from this modality is an extremely challenging task. Visual images with
significantly lesser amount of distortions can act as a bridge for the analysis
of caricature modality. We introduce a publicly available large
Caricature-VIsual dataset [CaVI] with images from both the modalities that
captures the rich variations in the caricature of an identity. This paper
presents the first cross modal architecture that handles extreme distortions of
caricatures using a deep learning network that learns similar representations
across the modalities. We use two convolutional networks along with
transformations that are subjected to orthogonality constraints to capture the
shared and modality specific representations. In contrast to prior research,
our approach neither depends on manually extracted facial landmarks for
learning the representations, nor on the identities of the person for
performing verification. The learned shared representation achieves 91%
accuracy for verifying unseen images and 75% accuracy on unseen identities.
Further, recognizing the identity in the image by knowledge transfer using a
combination of shared and modality specific representations, resulted in an
unprecedented performance of 85% rank-1 accuracy for caricatures and 95% rank-1
accuracy for visual images.
Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power
Authors: Asifullah Khan, Aneela Zameer, Tauseef Jamal, Ahmad Raza
Date: 2018-07-31 00:00:00
Tasks: Multi-Task Learning, Prediction, regression, Unsupervised Pre-training

Wind energy forecasting helps to manage power production, and hence, reduces
energy cost. Deep Neural Networks (DNN) mimics hierarchical learning in the
human brain and thus possesses hierarchical, distributed, and multi-task
learning capabilities. Based on aforementioned characteristics, we report Deep
Belief Network (DBN) based forecast engine for wind power prediction because of
its good generalization and unsupervised pre-training attributes. The proposed
DBN-WP forecast engine, which exhibits stochastic feature generation
capabilities and is composed of multiple Restricted Boltzmann Machines,
generates suitable features for wind power prediction using atmospheric
properties as input. DBN-WP, due to its unsupervised pre-training of RBM layers
and generalization capabilities, is able to learn the fluctuations in the
meteorological properties and thus is able to perform effective mapping of the
wind power. In the deep network, a regression layer is appended at the end to
predict sort-term wind power. It is experimentally shown that the deep learning
and unsupervised pre-training capabilities of DBN based model has comparable
and in some cases better results than hybrid and complex learning techniques
proposed for wind power prediction. The proposed prediction system based on
DBN, achieves mean values of RMSE, MAE and SDE as 0.124, 0.083 and 0.122,
respectively. Statistical analysis of several independent executions of the
proposed DBN-WP wind power prediction system demonstrates the stability of the
system. The proposed DBN-WP architecture is easy to implement and offers
generalization as regards the change in location of the wind farm is concerned.
Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder
Authors: Yi Zhao, Shinji Takaki, Hieu-Thi Luong, Junichi Yamagishi, Daisuke Saito, Nobuaki Minematsu
Date: 2018-07-31 00:00:00
Tasks: Generative Adversarial Network, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Recent neural networks such as WaveNet and sampleRNN that learn directly from
speech waveform samples have achieved very high-quality synthetic speech in
terms of both naturalness and speaker similarity even in multi-speaker
text-to-speech synthesis systems. Such neural networks are being used as an
alternative to vocoders and hence they are often called neural vocoders. The
neural vocoder uses acoustic features as local condition parameters, and these
parameters need to be accurately predicted by another acoustic model. However,
it is not yet clear how to train this acoustic model, which is problematic
because the final quality of synthetic speech is significantly affected by the
performance of the acoustic model. Significant degradation happens, especially
when predicted acoustic features have mismatched characteristics compared to
natural ones. In order to reduce the mismatched characteristics between natural
and generated acoustic features, we propose frameworks that incorporate either
a conditional generative adversarial network (GAN) or its variant, Wasserstein
GAN with gradient penalty (WGAN-GP), into multi-speaker speech synthesis that
uses the WaveNet vocoder. We also extend the GAN frameworks and use the
discretized mixture logistic loss of a well-trained WaveNet in addition to mean
squared error and adversarial losses as parts of objective functions.
Experimental results show that acoustic models trained using the WGAN-GP
framework using back-propagated discretized-mixture-of-logistics (DML) loss
achieves the highest subjective evaluation scores in terms of both quality and
speaker similarity.
Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection
Authors: Saad Ullah Akram, Talha Qaiser, Simon Graham, Juho Kannala, Janne Heikkilä, Nasir Rajpoot
Date: 2018-07-31 00:00:00
Tasks: Mitosis Detection, Prognosis, whole slide images

Mitosis count is an important biomarker for prognosis of various cancers. At
present, pathologists typically perform manual counting on a few selected
regions of interest in breast whole-slide-images (WSIs) of patient biopsies.
This task is very time-consuming, tedious and subjective. Automated mitosis
detection methods have made great advances in recent years. However, these
methods require exhaustive labeling of a large number of selected regions of
interest. This task is very expensive because expert pathologists are needed
for reliable and accurate annotations. In this paper, we present a
semi-supervised mitosis detection method which is designed to leverage a large
number of unlabeled breast cancer WSIs. As a result, our method capitalizes on
the growing number of digitized histology images, without relying on exhaustive
annotations, subsequently improving mitosis detection. Our method first learns
a mitosis detector from labeled data, uses this detector to mine additional
mitosis samples from unlabeled WSIs, and then trains the final model using this
larger and diverse set of mitosis samples. The use of unlabeled data improves
F1-score by $\sim$5\% compared to our best performing fully-supervised model on
the TUPAC validation set. Our submission (single model) to TUPAC challenge
ranks highly on the leaderboard with an F1-score of 0.64.
Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition
Authors: Roshanak Zakizadeh, Michele Sasdelli, Yu Qian, Eduard Vazquez
Date: 2018-07-31 00:00:00
Tasks: Attribute

DeepFashion is a widely used clothing dataset with 50 categories and more
than overall 200k images where each image is annotated with fine-grained
attributes. This dataset is often used for clothes recognition and although it
provides comprehensive annotations, the attributes distribution is unbalanced
and repetitive specially for training fine-grained attribute recognition
models. In this work, we tailored DeepFashion for fine-grained attribute
recognition task by focusing on each category separately. After selecting
categories with sufficient number of images for training, we remove very scarce
attributes and merge the duplicate ones in each category, then we clean the
dataset based on the new list of attributes. We use a bilinear convolutional
neural network with pairwise ranking loss function for multi-label fine-grained
attribute recognition and show that the new annotations improve the results for
such a task. The detailed annotations for each of the selected categories are
provided for public use.
Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II
Authors: Shih-Huan Hsu, Tian-Li Yu
Date: 2018-07-31 00:00:00
Tasks: Efficient Exploration

This paper proposes a new evolutionary algorithm, called DSMGA-II, to
efficiently solve optimization problems via exploiting problem substructures.
The proposed algorithm adopts pairwise linkage detection and stores the
information in the form of dependency structure matrix (DSM). A new linkage
model, called the incremental linkage set, is then constructed by using the
DSM. Inspired by the idea of optimal mixing, the restricted mixing and the back
mixing are proposed. The former aims at efficient exploration under certain
constrains. The latter aims at exploitation by refining the DSM so as to reduce
unnecessary evaluations. Experimental results show that DSMGA-II outperforms
LT-GOMEA and hBOA in terms of number of function evaluations on the
concatenated/folded/cyclic trap problems, NK-landscape problems with various
degrees of overlapping, 2D Ising spin-glass problems, and MAX-SAT. The
investigation of performance comparison with P3 is also included.
Security and Privacy Issues in Deep Learning
Authors: Ho Bae, Jaehee Jang, Dahuin Jung, Hyemi Jang, Heonseok Ha, Hyungyu Lee, Sungroh Yoon
Date: 2018-07-31 00:00:00
Tasks: Deep Learning

To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended without being affected by malicious external influences that can compromise its integrity and efficiency. Security attacks can be divided based on when they occur: if an attack occurs during training, it is known as a poisoning attack, and if it occurs during inference (after training) it is termed an evasion attack. Poisoning attacks compromise the training process by corrupting the data with malicious examples, while evasion attacks use adversarial examples to disrupt entire classification process. Defenses proposed against such attacks include techniques to recognize and remove malicious data, train a model to be insensitive to such data, and mask the model's structure and parameters to render attacks more challenging to implement. Furthermore, the privacy of the data involved in model training is also threatened by attacks such as the model-inversion attack, or by dishonest service providers of AI applications. To maintain data privacy, several solutions that combine existing data-privacy techniques have been proposed, including differential privacy and modern cryptography techniques. In this paper, we describe the notions of some of methods, e.g., homomorphic encryption, and review their advantages and challenges when implemented in deep-learning models.
The Devil of Face Recognition is in the Noise
Authors: Fei Wang, Liren Chen, Cheng Li, Shiyao Huang, Yanjie Chen, Chen Qian, Chen Change Loy
Date: 2018-07-31 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Recognition

The growing scale of face recognition datasets empowers us to train strong
convolutional networks for face recognition. While a variety of architectures
and loss functions have been devised, we still have a limited understanding of
the source and consequence of label noise inherent in existing datasets. We
make the following contributions: 1) We contribute cleaned subsets of popular
face databases, i.e., MegaFace and MS-Celeb-1M datasets, and build a new
large-scale noise-controlled IMDb-Face dataset. 2) With the original datasets
and cleaned subsets, we profile and analyze label noise properties of MegaFace
and MS-Celeb-1M. We show that a few orders more samples are needed to achieve
the same accuracy yielded by a clean subset. 3) We study the association
between different types of noise, i.e., label flips and outliers, with the
accuracy of face recognition models. 4) We investigate ways to improve data
cleanliness, including a comprehensive user study on the influence of data
labeling strategies to annotation accuracy. The IMDb-Face dataset has been
released on https://github.com/fwang91/IMDb-Face.
Composable Core-sets for Determinant Maximization Problems via Spectral Spanners
Authors: Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, Alireza Rezaei
Date: 2018-07-31 00:00:00

We study a spectral generalization of classical combinatorial graph spanners to the spectral setting. Given a set of vectors $V\subseteq \Re^d$, we say a set $U\subseteq V$ is an $\alpha$-spectral spanner if for all $v\in V$ there is a probability distribution $\mu_v$ supported on $U$ such that $$vv^\intercal \preceq \alpha\cdot\mathbb{E}_{u\sim\mu_v} uu^\intercal.$$ We show that any set $V$ has an $\tilde{O}(d)$-spectral spanner of size $\tilde{O}(d)$ and this bound is almost optimal in the worst case. We use spectral spanners to study composable core-sets for spectral problems. We show that for many objective functions one can use a spectral spanner, independent of the underlying functions, as a core-set and obtain almost optimal composable core-sets. For example, for the determinant maximization problem we obtain an $\tilde{O}(k)^k$-composable core-set and we show that this is almost optimal in the worst case. Our algorithm is a spectral analogue of the classical greedy algorithm for finding (combinatorial) spanners in graphs. We expect that our spanners find many other applications in distributed or parallel models of computation. Our proof is spectral. As a side result of our techniques, we show that the rank of diagonally dominant lower-triangular matrices are robust under `small perturbations' which could be of independent interests.
Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer Learning
Authors: Sifeng He, Bahram Jalali
Date: 2018-07-31 00:00:00
Tasks: Hallucination, Image Super-Resolution, Medical Super-Resolution, Super-Resolution, Transfer Learning

A hallucination-free and computationally efficient algorithm for enhancing
the resolution of brain MRI images is demonstrated.