pwc-archive/papers-with-abstracts huggingface dataset:
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Authors: Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, DaCheng Tao
Date: 2019-04-14 00:00:00
Tasks: Active Learning, Multi-Label Learning

Multi-label learning draws great interests in many real world applications.
It is a highly costly task to assign many labels by the oracle for one
instance. Meanwhile, it is also hard to build a good model without diagnosing
discriminative labels. Can we reduce the label costs and improve the ability to
train a good model for multi-label learning simultaneously?
  Active learning addresses the less training samples problem by querying the
most valuable samples to achieve a better performance with little costs. In
multi-label active learning, some researches have been done for querying the
relevant labels with less training samples or querying all labels without
diagnosing the discriminative information. They all cannot effectively handle
the outlier labels for the measurement of uncertainty. Since Maximum
Correntropy Criterion (MCC) provides a robust analysis for outliers in many
machine learning and data mining algorithms, in this paper, we derive a robust
multi-label active learning algorithm based on MCC by merging uncertainty and
representativeness, and propose an efficient alternating optimization method to
solve it. With MCC, our method can eliminate the influence of outlier labels
that are not discriminative to measure the uncertainty. To make further
improvement on the ability of information measurement, we merge uncertainty and
representativeness with the prediction labels of unknown data. It can not only
enhance the uncertainty but also improve the similarity measurement of
multi-label data with labels information. Experiments on benchmark multi-label
data sets have shown a superior performance than the state-of-the-art methods.
Exploring Representativeness and Informativeness for Active Learning
Authors: Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Wei Liu, Jialie Shen, DaCheng Tao
Date: 2019-04-14 00:00:00
Tasks: Active Learning, Informativeness

How can we find a general way to choose the most suitable samples for
training a classifier? Even with very limited prior information? Active
learning, which can be regarded as an iterative optimization procedure, plays a
key role to construct a refined training set to improve the classification
performance in a variety of applications, such as text analysis, image
recognition, social network modeling, etc.
  Although combining representativeness and informativeness of samples has been
proven promising for active sampling, state-of-the-art methods perform well
under certain data structures. Then can we find a way to fuse the two active
sampling criteria without any assumption on data? This paper proposes a general
active learning framework that effectively fuses the two criteria. Inspired by
a two-sample discrepancy problem, triple measures are elaborately designed to
guarantee that the query samples not only possess the representativeness of the
unlabeled data but also reveal the diversity of the labeled data. Any
appropriate similarity measure can be employed to construct the triple
measures. Meanwhile, an uncertain measure is leveraged to generate the
informativeness criterion, which can be carried out in different ways.
  Rooted in this framework, a practical active learning algorithm is proposed,
which exploits a radial basis function together with the estimated
probabilities to construct the triple measures and a modified
Best-versus-Second-Best strategy to construct the uncertain measure,
respectively. Experimental results on benchmark datasets demonstrate that our
algorithm consistently achieves superior performance over the state-of-the-art
active learning algorithms.
Lunar surface image restoration using U-net based deep neural networks
Authors: Hiya Roy, Subhajit Chaudhury, Toshihiko Yamasaki, Danielle DeLatte, Makiko Ohtake, Tatsuaki Hashimoto
Date: 2019-04-14 00:00:00
Tasks: Image Inpainting, Image Restoration

Image restoration is a technique that reconstructs a feasible estimate of the
original image from the noisy observation. In this paper, we present a U-Net
based deep neural network model to restore the missing pixels on the lunar
surface image in a context-aware fashion, which is often known as image
inpainting problem. We use the grayscale image of the lunar surface captured by
Multiband Imager (MI) onboard Kaguya satellite for our experiments and the
results show that our method can reconstruct the lunar surface image with good
visual quality and improved PSNR values.
From News to Medical: Cross-domain Discourse Segmentation
Authors: Elisa Ferracane, Titan Page, Junyi Jessy Li, Katrin Erk
Date: 2019-04-14 00:00:00
Proceeding: WS 2019 6
Tasks: Discourse Segmentation

The first step in discourse analysis involves dividing a text into segments.
We annotate the first high-quality small-scale medical corpus in English with
discourse segments and analyze how well news-trained segmenters perform on this
domain. While we expectedly find a drop in performance, the nature of the
segmentation errors suggests some problems can be addressed earlier in the
pipeline, while others would require expanding the corpus to a trainable size
to learn the nuances of the medical domain.
RelEmb: A relevance-based application embedding for Mobile App retrieval and categorization
Authors: Ahsaas Bajaj, Shubham Krishna, Mukund Rungta, Hemant Tiwari, Vanraj Vala
Date: 2019-04-14 00:00:00
Tasks: Clustering, General Classification, Information Retrieval, Retrieval

Information Retrieval Systems have revolutionized the organization and
extraction of Information. In recent years, mobile applications (apps) have
become primary tools of collecting and disseminating information. However,
limited research is available on how to retrieve and organize mobile apps on
users' devices. In this paper, authors propose a novel method to estimate
app-embeddings which are then applied to tasks like app clustering,
classification, and retrieval. Usage of app-embedding for query expansion,
nearest neighbor analysis enables unique and interesting use cases to enhance
end-user experience with mobile apps.
EXPERTNet Exigent Features Preservative Network for Facial Expression Recognition
Authors: Monu Verma, Jaspreet Kaur Bhui, Santosh Vipparthi, Girdhari Singh
Date: 2019-04-14 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Facial expressions have essential cues to infer the humans state of mind,
that conveys adequate information to understand individuals actual feelings.
Thus, automatic facial expression recognition is an interesting and crucial
task to interpret the humans cognitive state through the machine. In this
paper, we proposed an Exigent Features Preservative Network (EXPERTNet), to
describe the features of the facial expressions. The EXPERTNet extracts only
pertinent features and neglect others by using exigent feature (ExFeat) block,
mainly comprises of elective layer. Specifically, elective layer selects the
desired edge variation features from the previous layer outcomes, which are
generated by applying different sized filters as 1 x 1, 3 x 3, 5 x 5 and 7 x 7.
Different sized filters aid to elicits both micro and high-level features that
enhance the learnability of neurons. ExFeat block preserves the spatial
structural information of the facial expression, which allows to discriminate
between different classes of facial expressions. Visual representation of the
proposed method over different facial expressions shows the learning capability
of the neurons of different layers. Experimental and comparative analysis
results over four comprehensive datasets CK+, MMI DISFA and GEMEP-FERA, ensures
the better performance of the proposed network as compared to existing
networks.
A Hybrid Traffic Speed Forecasting Approach Integrating Wavelet Transform and Motif-based Graph Convolutional Recurrent Neural Network
Authors: Na Zhang, Xuefeng Guan, Jun Cao, Xinglei Wang, Huayi Wu
Date: 2019-04-14 00:00:00
Tasks: Management

Traffic forecasting is crucial for urban traffic management and guidance.
However, existing methods rarely exploit the time-frequency properties of
traffic speed observations, and often neglect the propagation of traffic flows
from upstream to downstream road segments. In this paper, we propose a hybrid
approach that learns the spatio-temporal dependency in traffic flows and
predicts short-term traffic speeds on a road network. Specifically, we employ
wavelet transform to decompose raw traffic data into several components with
different frequency sub-bands. A Motif-based Graph Convolutional Recurrent
Neural Network (Motif-GCRNN) and Auto-Regressive Moving Average (ARMA) are used
to train and predict low-frequency components and high-frequency components,
respectively. In the Motif-GCRNN framework, we integrate Graph Convolutional
Networks (GCNs) with local sub-graph structures - Motifs - to capture the
spatial correlations among road segments, and apply Long Short-Term Memory
(LSTM) to extract the short-term and periodic patterns in traffic speeds.
Experiments on a traffic dataset collected in Chengdu, China, demonstrate that
the proposed hybrid method outperforms six state-of-art prediction methods.
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering
Authors: Wei Yang, Yuqing Xie, Luchen Tan, Kun Xiong, Ming Li, Jimmy Lin
Date: 2019-04-14 00:00:00
Tasks: Data Augmentation, Open-Domain Question Answering, Passage Retrieval, Question Answering, Retrieval

Recently, a simple combination of passage retrieval using off-the-shelf IR
techniques and a BERT reader was found to be very effective for question
answering directly on Wikipedia, yielding a large improvement over the previous
state of the art on a standard benchmark dataset. In this paper, we present a
data augmentation technique using distant supervision that exploits positive as
well as negative examples. We apply a stage-wise approach to fine tuning BERT
on multiple datasets, starting with data that is "furthest" from the test data
and ending with the "closest". Experimental results show large gains in
effectiveness over previous approaches on English QA datasets, and we establish
new baselines on two recent Chinese QA datasets.
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms
Authors: Zahra Zohrevand, Uwe Glässer
Date: 2019-04-14 00:00:00
Tasks: Anomaly Detection, Intrusion Detection, Management

Nowadays, advanced intrusion detection systems (IDSs) rely on a combination of anomaly detection and signature-based methods. An IDS gathers observations, analyzes behavioral patterns, and reports suspicious events for further investigation. A notorious issue anomaly detection systems (ADSs) and IDSs face is the possibility of high false alarms, which even state-of-the-art systems have not overcome. This is especially a problem with large and complex systems. The number of non-critical alarms can easily overwhelm administrators and increase the likelihood of ignoring future alerts. Mitigation strategies thus aim to avoid raising `too many' false alarms without missing potentially dangerous situations. There are two major categories of false alarm-mitigation strategies: (1) methods that are customized to enhance the quality of anomaly scoring; (2) approaches acting as filtering methods in contexts that aim to decrease false alarm rates. These methods have been widely utilized by many scholars. Herein, we review and compare the existing techniques for false alarm mitigation in ADSs. We also examine the use of promising techniques in signature-based IDS and other relevant contexts, such as commercial security information and event management tools, which are promising for ADSs. We conclude by highlighting promising directions for future research.
Localizing Discriminative Visual Landmarks for Place Recognition
Authors: Zhe Xin, Yinghao Cai, Tao Lu, Xiaoxia Xing, Shaojun Cai, Jixiang Zhang, Yiping Yang, Yanqing Wang
Date: 2019-04-14 00:00:00
Tasks: Visual Place Recognition

We address the problem of visual place recognition with perceptual changes.
The fundamental problem of visual place recognition is generating robust image
representations which are not only insensitive to environmental changes but
also distinguishable to different places. Taking advantage of the feature
extraction ability of Convolutional Neural Networks (CNNs), we further
investigate how to localize discriminative visual landmarks that positively
contribute to the similarity measurement, such as buildings and vegetations. In
particular, a Landmark Localization Network (LLN) is designed to indicate which
regions of an image are used for discrimination. Detailed experiments are
conducted on open source datasets with varied appearance and viewpoint changes.
The proposed approach achieves superior performance against state-of-the-art
methods.
Unsupervised Synthesis of Anomalies in Videos: Transforming the Normal
Authors: Abhishek Joshi, Vinay P. Namboodiri
Date: 2019-04-14 00:00:00
Tasks: Activity Recognition, Data Augmentation

Abnormal activity recognition requires detection of occurrence of anomalous
events that suffer from a severe imbalance in data. In a video, normal is used
to describe activities that conform to usual events while the irregular events
which do not conform to the normal are referred to as abnormal. It is far more
common to observe normal data than to obtain abnormal data in visual
surveillance. In this paper, we propose an approach where we can obtain
abnormal data by transforming normal data. This is a challenging task that is
solved through a multi-stage pipeline approach. We utilize a number of
techniques from unsupervised segmentation in order to synthesize new samples of
data that are transformed from an existing set of normal examples. Further,
this synthesis approach has useful applications as a data augmentation
technique. An incrementally trained Bayesian convolutional neural network (CNN)
is used to carefully select the set of abnormal samples that can be added.
Finally through this synthesis approach we obtain a comparable set of abnormal
samples that can be used for training the CNN for the classification of normal
vs abnormal samples. We show that this method generalizes to multiple settings
by evaluating it on two real world datasets and achieves improved performance
over other probabilistic techniques that have been used in the past for this
task.
Analysis of overfitting in the regularized Cox model
Authors: M Sheikh, A. C. C. Coolen
Date: 2019-04-14 00:00:00
Tasks: L2 Regularization, model, regression

The Cox proportional hazards model is ubiquitous in the analysis of time-to-event data. However, when the data dimension p is comparable to the sample size $N$, maximum likelihood estimates for its regression parameters are known to be biased or break down entirely due to overfitting. This prompted the introduction of the so-called regularized Cox model. In this paper we use the replica method from statistical physics to investigate the relationship between the true and inferred regression parameters in regularized multivariate Cox regression with L2 regularization, in the regime where both p and N are large but with p/N ~ O(1). We thereby generalize a recent study from maximum likelihood to maximum a posteriori inference. We also establish a relationship between the optimal regularization parameter and p/N, allowing for straightforward overfitting corrections in time-to-event analysis.
Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning
Authors: Xun Wang, Xintong Han, Weilin Huang, Dengke Dong, Matthew R. Scott
Date: 2019-04-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Retrieval, Metric Learning, Retrieval

A family of loss functions built on pair-based computation have been proposed in the literature which provide a myriad of solutions for deep metric learning. In this paper, we provide a general weighting framework for understanding recent pair-based loss functions. Our contributions are three-fold: (1) we establish a General Pair Weighting (GPW) framework, which casts the sampling problem of deep metric learning into a unified view of pair weighting through gradient analysis, providing a powerful tool for understanding recent pair-based loss functions; (2) we show that with GPW, various existing pair-based methods can be compared and discussed comprehensively, with clear differences and key limitations identified; (3) we propose a new loss called multi-similarity loss (MS loss) under the GPW, which is implemented in two iterative steps (i.e., mining and weighting). This allows it to fully consider three similarities for pair weighting, providing a more principled approach for collecting and weighting informative pairs. Finally, the proposed MS loss obtains new state-of-the-art performance on four image retrieval benchmarks, where it outperforms the most recent approaches, such as ABE\cite{Kim_2018_ECCV} and HTL by a large margin: 60.6% to 65.7% on CUB200, and 80.9% to 88.0% on In-Shop Clothes Retrieval dataset at Recall@1. Code is available at https://github.com/MalongTech/research-ms-loss.
Biphasic Learning of GANs for High-Resolution Image-to-Image Translation
Authors: Jie Cao, Huaibo Huang, Yi Li, Jingtuo Liu, Ran He, Zhenan Sun
Date: 2019-04-14 00:00:00
Tasks: Image-to-Image Translation, Mutual Information Estimation, Translation, Vocal Bursts Intensity Prediction

Despite that the performance of image-to-image translation has been
significantly improved by recent progress in generative models, current methods
still suffer from severe degradation in training stability and sample quality
when applied to the high-resolution situation. In this work, we present a novel
training framework for GANs, namely biphasic learning, to achieve
image-to-image translation in multiple visual domains at $1024^2$ resolution.
Our core idea is to design an adjustable objective function that varies across
training phases. Within the biphasic learning framework, we propose a novel
inherited adversarial loss to achieve the enhancement of model capacity and
stabilize the training phase transition. Furthermore, we introduce a
perceptual-level consistency loss through mutual information estimation and
maximization. To verify the superiority of the proposed method, we apply it to
a wide range of face-related synthesis tasks and conduct experiments on
multiple large-scale datasets. Through comprehensive quantitative analyses, we
demonstrate that our method significantly outperforms existing methods.
UR-FUNNY: A Multimodal Language Dataset for Understanding Humor
Authors: Md. Kamrul Hasan, Wasifur Rahman, Amir Zadeh, Jianyuan Zhong, Md. Iftekhar Tanveer, Louis-Philippe Morency, Mohammed, Hoque
Date: 2019-04-14 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Humor Detection

Humor is a unique and creative communicative behavior displayed during social
interactions. It is produced in a multimodal manner, through the usage of words
(text), gestures (vision) and prosodic cues (acoustic). Understanding humor
from these three modalities falls within boundaries of multimodal language; a
recent research trend in natural language processing that models natural
language as it happens in face-to-face communication. Although humor detection
is an established research area in NLP, in a multimodal context it is an
understudied area. This paper presents a diverse multimodal dataset, called
UR-FUNNY, to open the door to understanding multimodal language used in
expressing humor. The dataset and accompanying studies, present a framework in
multimodal humor detection for the natural language processing community.
UR-FUNNY is publicly available for research.
LiveSketch: Query Perturbations for Guided Sketch-based Visual Search
Authors: John Collomosse, Tu Bui, Hailin Jin
Date: 2019-04-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, Triplet

LiveSketch is a novel algorithm for searching large image collections using
hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch
search by creating visual suggestions that augment the query as it is drawn,
making query specification an iterative rather than one-shot process that helps
disambiguate users' search intent. Our technical contributions are: a triplet
convnet architecture that incorporates an RNN based variational autoencoder to
search for images using vector (stroke-based) queries; real-time clustering to
identify likely search intents (and so, targets within the search embedding);
and the use of backpropagation from those targets to perturb the input stroke
sequence, so suggesting alterations to the query in order to guide the search.
We show improvements in accuracy and time-to-task over contemporary baselines
using a 67M image corpus.
Shakeout: A New Approach to Regularized Deep Neural Network Training
Authors: Guoliang Kang, Jun Li, DaCheng Tao
Date: 2019-04-13 00:00:00
Tasks: Model Compression

Recent years have witnessed the success of deep neural networks in dealing
with a plenty of practical problems. Dropout has played an essential role in
many successful deep neural networks, by inducing regularization in the model
training. In this paper, we present a new regularized training approach:
Shakeout. Instead of randomly discarding units as Dropout does at the training
stage, Shakeout randomly chooses to enhance or reverse each unit's contribution
to the next layer. This minor modification of Dropout has the statistical
trait: the regularizer induced by Shakeout adaptively combines $L_0$, $L_1$ and
$L_2$ regularization terms. Our classification experiments with representative
deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that
Shakeout deals with over-fitting effectively and outperforms Dropout. We
empirically demonstrate that Shakeout leads to sparser weights under both
unsupervised and supervised settings. Shakeout also leads to the grouping
effect of the input units in a layer. Considering the weights in reflecting the
importance of connections, Shakeout is superior to Dropout, which is valuable
for the deep model compression. Moreover, we demonstrate that Shakeout can
effectively reduce the instability of the training process of the deep
architecture.
Unsupervised Singing Voice Conversion
Authors: Eliya Nachmani, Lior Wolf
Date: 2019-04-13 00:00:00
Tasks: Data Augmentation, Decoder, Voice Conversion

We present a deep learning method for singing voice conversion. The proposed network is not conditioned on the text or on the notes, and it directly converts the audio of one singer to the voice of another. Training is performed without any form of supervision: no lyrics or any kind of phonetic features, no notes, and no matching samples between singers. The proposed network employs a single CNN encoder for all singers, a single WaveNet decoder, and a classifier that enforces the latent representation to be singer-agnostic. Each singer is represented by one embedding vector, which the decoder is conditioned on. In order to deal with relatively small datasets, we propose a new data augmentation scheme, as well as new training losses and protocols that are based on backtranslation. Our evaluation presents evidence that the conversion produces natural signing voices that are highly recognizable as the target singer.