pwc-archive/papers-with-abstracts huggingface dataset:
Changing the Image Memorability: From Basic Photo Editing to GANs
Authors: Oleksii Sidorov
Date: 2018-11-09 00:00:00
Tasks: Attribute

Memorability is considered to be an important characteristic of visual
content, whereas for advertisement and educational purposes it is often
crucial. Despite numerous studies on understanding and predicting image
memorability, there are almost no achievements in memorability modification. In
this work, we study two approaches to image editing - GAN and classical image
processing - and show their impact on memorability. The visual features which
influence memorability directly stay unknown till now, hence it is impossible
to control it manually. As a solution, we let GAN learn it deeply using labeled
data, and then use it for conditional generation of new images. By analogy with
algorithms which edit facial attributes, we consider memorability as yet
another attribute and operate with it in the same way. Obtained data is also
interesting for analysis, simply because there are no real-world examples of
successful change of image memorability while preserving its other attributes.
We believe this may give many new answers to the question "what makes an image
memorable?" Apart from that we also study the influence of conventional
photo-editing tools (Photoshop, Instagram, etc.) used daily by a wide audience
on memorability. In this case, we start from real practical methods and study
it using statistics and recent advances in memorability prediction.
Photographers, designers, and advertisers will benefit from the results of this
study directly.
A Very Brief and Critical Discussion on AutoML
Authors: Bin Liu
Date: 2018-11-09 00:00:00
Tasks: AutoML

This contribution presents a very brief and critical discussion on automated
machine learning (AutoML), which is categorized here into two classes, referred
to as narrow AutoML and generalized AutoML, respectively. The conclusions
yielded from this discussion can be summarized as follows: (1) most existent
research on AutoML belongs to the class of narrow AutoML; (2) advances in
narrow AutoML are mainly motivated by commercial needs, while any possible
benefit obtained is definitely at a cost of increase in computing burdens;
(3)the concept of generalized AutoML has a strong tie in spirit with artificial
general intelligence (AGI), also called "strong AI", for which obstacles abound
for obtaining pivotal progresses.
Skeptical Deep Learning with Distribution Correction
Authors: Mingxiao An, Yongzhou Chen, Qi Liu, Chuanren Liu, Guangyi Lv, Fangzhao Wu, Jianhui Ma
Date: 2018-11-09 00:00:00
Tasks: Classification, Deep Learning, General Classification

Recently deep neural networks have been successfully used for various
classification tasks, especially for problems with massive perfectly labeled
training data. However, it is often costly to have large-scale credible labels
in real-world applications. One solution is to make supervised learning robust
with imperfectly labeled input. In this paper, we develop a distribution
correction approach that allows deep neural networks to avoid overfitting
imperfect training data. Specifically, we treat the noisy input as samples from
an incorrect distribution, which will be automatically corrected during our
training process. We test our approach on several classification datasets with
elaborately generated noisy labels. The results show significantly higher
prediction and recovery accuracy with our approach compared to alternative
methods.
The Price of Governance: A Middle Ground Solution to Coordination in Organizational Control
Authors: Chao Yu
Date: 2018-11-09 00:00:00

Achieving coordination is crucial in organizational control. This paper
investigates a middle ground solution between decentralized interactions and
centralized administrations for coordinating agents beyond inefficient
behavior. We first propose the price of governance (PoG) to evaluate how such a
middle ground solution performs in terms of effectiveness and cost. We then
propose a hierarchical supervision framework to explicitly model the PoG, and
define step by step how to realize the core principle of the framework and
compute the optimal PoG for a control problem. Two illustrative case studies
are carried out to exemplify the applications of the proposed framework and its
methodology. Results show that by properly formulating and implementing each
step, the hierarchical supervision framework is capable of promoting
coordination among agents while bounding administrative cost to a minimum in
different kinds of organizational control problems.
RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement
Authors: Kiwoo Shin, Youngwook Paul Kwon, Masayoshi Tomizuka
Date: 2018-11-09 00:00:00
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Robust 3D Object Detection

We present RoarNet, a new approach for 3D object detection from a 2D image
and 3D Lidar point clouds. Based on two-stage object detection framework with
PointNet as our backbone network, we suggest several novel ideas to improve 3D
object detection performance. The first part of our method, RoarNet_2D,
estimates the 3D poses of objects from a monocular image, which approximates
where to examine further, and derives multiple candidates that are
geometrically feasible. This step significantly narrows down feasible 3D
regions, which otherwise requires demanding processing of 3D point clouds in a
huge search space. Then the second part, RoarNet_3D, takes the candidate
regions and conducts in-depth inferences to conclude final poses in a recursive
manner. Inspired by PointNet, RoarNet_3D processes 3D point clouds directly
without any loss of data, leading to precise detection. We evaluate our method
in KITTI, a 3D object detection benchmark. Our result shows that RoarNet has
superior performance to state-of-the-art methods that are publicly available.
Remarkably, RoarNet also outperforms state-of-the-art methods even in settings
where Lidar and camera are not time synchronized, which is practically
important for actual driving environments. RoarNet is implemented in Tensorflow
and publicly available with pre-trained models.
Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images
Authors: Edwin Yuan, Junkyo Suh
Date: 2018-11-09 00:00:00
Tasks: Cell Segmentation, Clustering, General Classification, Generative Adversarial Network, Segmentation

In this paper, we develop a complete pipeline for stain normalization,
segmentation, and classification of nuclei in hematoxylin and eosin (H&E)
stained breast cancer histopathology images. In the first step, we use a
CNN-based stain transfer technique to normalize the staining characteristics of
(H&E) images. We then train a neural network to segment images of nuclei from
the H&E images. Finally, we train an Information Maximizing Generative
Adversarial Network (InfoGAN) to learn visual representations of different
types of nuclei and classify them in an entirely unsupervised manner. The
results show that our proposed CNN stain normalization yields improved visual
similarity and cell segmentation performance compared to the conventional
SVD-based stain normalization method. In the final step of our pipeline, we
demonstrate the ability to perform fully unsupervised clustering of various
breast histopathology cell types based on morphological and color attributes.
In addition, we quantitatively evaluate our neural network - based techniques
against various quantitative metrics to validate the effectiveness of our
pipeline.
Gradient Descent Finds Global Minima of Deep Neural Networks
Authors: Simon S. Du, Jason D. Lee, Haochuan Li, Li-Wei Wang, Xiyu Zhai
Date: 2018-11-09 00:00:00

Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture. This structure allows us to show the Gram matrix is stable throughout the training process and this stability implies the global optimality of the gradient descent algorithm. We further extend our analysis to deep residual convolutional neural networks and obtain a similar convergence result.
Encoding Implicit Relation Requirements for Relation Extraction: A Joint Inference Approach
Authors: Li-Wei Chen, Yansong Feng, Songfang Huang, Bingfeng Luo, Dongyan Zhao
Date: 2018-11-09 00:00:00
Tasks: Knowledge Base Construction, Question Answering, Relation, Relation Extraction

Relation extraction is the task of identifying predefined relationship
between entities, and plays an essential role in information extraction,
knowledge base construction, question answering and so on. Most existing
relation extractors make predictions for each entity pair locally and
individually, while ignoring implicit global clues available across different
entity pairs and in the knowledge base, which often leads to conflicts among
local predictions from different entity pairs. This paper proposes a joint
inference framework that employs such global clues to resolve disagreements
among local predictions. We exploit two kinds of clues to generate constraints
which can capture the implicit type and cardinality requirements of a relation.
Those constraints can be examined in either hard style or soft style, both of
which can be effectively explored in an integer linear program formulation.
Experimental results on both English and Chinese datasets show that our
proposed framework can effectively utilize those two categories of global clues
and resolve the disagreements among local predictions, thus improve various
relation extractors when such clues are applicable to the datasets. Our
experiments also indicate that the clues learnt automatically from existing
knowledge bases perform comparably to or better than those refined by human.
Can We Use Speaker Recognition Technology to Attack Itself? Enhancing Mimicry Attacks Using Automatic Target Speaker Selection
Authors: Tomi Kinnunen, Rosa González Hautamäki, Ville Vestman, Md Sahidullah
Date: 2018-11-09 00:00:00
Tasks: Speaker Recognition, Speaker Verification

We consider technology-assisted mimicry attacks in the context of automatic
speaker verification (ASV). We use ASV itself to select targeted speakers to be
attacked by human-based mimicry. We recorded 6 naive mimics for whom we select
target celebrities from VoxCeleb1 and VoxCeleb2 corpora (7,365 potential
targets) using an i-vector system. The attacker attempts to mimic the selected
target, with the utterances subjected to ASV tests using an independently
developed x-vector system. Our main finding is negative: even if some of the
attacker scores against the target speakers were slightly increased, our mimics
did not succeed in spoofing the x-vector system. Interestingly, however, the
relative ordering of the selected targets (closest, furthest, median) are
consistent between the systems, which suggests some level of transferability
between the systems.
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI
Authors: Risheng Liu, Yuxi Zhang, Shichao Cheng, Xin Fan, Zhongxuan Luo
Date: 2018-11-09 00:00:00
Tasks: Compressive Sensing

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging
techniques available for clinical applications. However, the rather slow speed
of MRI acquisitions limits the patient throughput and potential indi cations.
Compressive Sensing (CS) has proven to be an efficient technique for
accelerating MRI acquisition. The most widely used CS-MRI model, founded on the
premise of reconstructing an image from an incompletely filled k-space, leads
to an ill-posed inverse problem. In the past years, lots of efforts have been
made to efficiently optimize the CS-MRI model. Inspired by deep learning
techniques, some preliminary works have tried to incorporate deep architectures
into CS-MRI process. Unfortunately, the convergence issues (due to the
experience-based networks) and the robustness (i.e., lack real-world noise
modeling) of these deeply trained optimization methods are still missing. In
this work, we develop a new paradigm to integrate designed numerical solvers
and the data-driven architectures for CS-MRI. By introducing an optimal
condition checking mechanism, we can successfully prove the convergence of our
established deep CS-MRI optimization scheme. Furthermore, we explicitly
formulate the Rician noise distributions within our framework and obtain an
extended CS-MRI network to handle the real-world nosies in the MRI process.
Extensive experimental results verify that the proposed paradigm outperforms
the existing state-of-the-art techniques both in reconstruction accuracy and
efficiency as well as robustness to noises in real scene.
A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs
Authors: Benjamin Johnston, Philip de Chazal
Date: 2018-11-09 00:00:00
Tasks: Facial Landmark Detection, Transfer Learning

We present a fully automated system for sizing nasal Positive Airway Pressure
(PAP) masks. The system is comprised of a mix of HOG object detectors as well
as multiple convolutional neural network stages for facial landmark detection.
The models were trained using samples from the publicly available PUT and MUCT
datasets while transfer learning was also employed to improve the performance
of the models on facial photographs of actual PAP mask users. The fully
automated system demonstrated an overall accuracy of 64.71% in correctly
selecting the appropriate mask size and 86.1% accuracy sizing within 1 mask
size.
M2M-GAN: Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification
Authors: Wenqi Liang, Guangcong Wang, Jian-Huang Lai, Junyong Zhu
Date: 2018-11-09 00:00:00
Tasks: Person Re-Identification, Transfer Learning

Cross-domain transfer learning (CDTL) is an extremely challenging task for
the person re-identification (ReID). Given a source domain with annotations and
a target domain without annotations, CDTL seeks an effective method to transfer
the knowledge from the source domain to the target domain. However, such a
simple two-domain transfer learning method is unavailable for the person ReID
in that the source/target domain consists of several sub-domains, e.g.,
camera-based sub-domains. To address this intractable problem, we propose a
novel Many-to-Many Generative Adversarial Transfer Learning method (M2M-GAN)
that takes multiple source sub-domains and multiple target sub-domains into
consideration and performs each sub-domain transferring mapping from the source
domain to the target domain in a unified optimization process. The proposed
method first translates the image styles of source sub-domains into that of
target sub-domains, and then performs the supervised learning by using the
transferred images and the corresponding annotations in source domain. As the
gap is reduced, M2M-GAN achieves a promising result for the cross-domain person
ReID. Experimental results on three benchmark datasets Market-1501,
DukeMTMC-reID and MSMT17 show the effectiveness of our M2M-GAN.
Typeface Completion with Generative Adversarial Networks
Authors: Yonggyu Park, Junhyun Lee, Yookyung Koh, Inyeop Lee, Jinhyuk Lee, Jaewoo Kang
Date: 2018-11-09 00:00:00
Tasks: Font Style Transfer, Image-to-Image Translation, Translation, Typeface Completion

The mood of a text and the intention of the writer can be reflected in the
typeface. However, in designing a typeface, it is difficult to keep the style
of various characters consistent, especially for languages with lots of
morphological variations such as Chinese. In this paper, we propose a Typeface
Completion Network (TCN) which takes one character as an input, and
automatically completes the entire set of characters in the same style as the
input characters. Unlike existing models proposed for image-to-image
translation, TCN embeds a character image into two separate vectors
representing typeface and content. Combined with a reconstruction loss from the
latent space, and with other various losses, TCN overcomes the inherent
difficulty in designing a typeface. Also, compared to previous image-to-image
translation models, TCN generates high quality character images of the same
typeface with a much smaller number of model parameters. We validate our
proposed model on the Chinese and English character datasets, which is paired
data, and the CelebA dataset, which is unpaired data. In these datasets, TCN
outperforms recently proposed state-of-the-art models for image-to-image
translation. The source code of our model is available at
https://github.com/yongqyu/TCN.
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets
Authors: Liping Li, Wei Xu, Tianyi Chen, Georgios B. Giannakis, Qing Ling
Date: 2018-11-09 00:00:00

In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine workers. The Byzantine workers, during the learning process, may send arbitrary incorrect messages to the master due to data corruptions, communication failures or malicious attacks, and consequently bias the learned model. The key to the proposed methods is a regularization term incorporated with the objective function so as to robustify the learning task and mitigate the negative effects of Byzantine attacks. The resultant subgradient-based algorithms are termed Byzantine-Robust Stochastic Aggregation methods, justifying our acronym RSA used henceforth. In contrast to most of the existing algorithms, RSA does not rely on the assumption that the data are independent and identically distributed (i.i.d.) on the workers, and hence fits for a wider class of applications. Theoretically, we show that: i) RSA converges to a near-optimal solution with the learning error dependent on the number of Byzantine workers; ii) the convergence rate of RSA under Byzantine attacks is the same as that of the stochastic gradient descent method, which is free of Byzantine attacks. Numerically, experiments on real dataset corroborate the competitive performance of RSA and a complexity reduction compared to the state-of-the-art alternatives.
EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction
Authors: Youru Li, Zhenfeng Zhu, Deqiang Kong, Hua Han, Yao Zhao
Date: 2018-11-09 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Prediction

Time series prediction with deep learning methods, especially long short-term
memory neural networks (LSTMs), have scored significant achievements in recent
years. Despite the fact that the LSTMs can help to capture long-term
dependencies, its ability to pay different degree of attention on sub-window
feature within multiple time-steps is insufficient. To address this issue, an
evolutionary attention-based LSTM training with competitive random search is
proposed for multivariate time series prediction. By transferring shared
parameters, an evolutionary attention learning approach is introduced to the
LSTMs model. Thus, like that for biological evolution, the pattern for
importance-based attention sampling can be confirmed during temporal
relationship mining. To refrain from being trapped into partial optimization
like traditional gradient-based methods, an evolutionary computation inspired
competitive random search method is proposed, which can well configure the
parameters in the attention layer. Experimental results have illustrated that
the proposed model can achieve competetive prediction performance compared with
other baseline methods.
Neural sequence labeling for Vietnamese POS Tagging and NER
Authors: Duong Nguyen Anh, Hieu Nguyen Kiem, Vi Ngo Van
Date: 2018-11-09 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging, Word Embeddings

This paper presents a neural architecture for Vietnamese sequence labeling
tasks including part-of-speech (POS) tagging and named entity recognition
(NER). We applied the model described in \cite{lample-EtAl:2016:N16-1} that is
a combination of bidirectional Long-Short Term Memory and Conditional Random
Fields, which rely on two sources of information about words: character-based
word representations learned from the supervised corpus and pre-trained word
embeddings learned from other unannotated corpora. Experiments on benchmark
datasets show that this work achieves state-of-the-art performances on both
tasks - 93.52\% accuracy for POS tagging and 94.88\% F1 for NER. Our sourcecode
is available at here.
DeepSaucer: Unified Environment for Verifying Deep Neural Networks
Authors: Naoto Sato, Hironobu Kuruma, Masanori Kaneko, Yuichiroh Nakagawa, Hideto Ogawa, Thai Son Hoang, Michael Butler
Date: 2018-11-09 00:00:00

In recent years, a number of methods for verifying DNNs have been developed.
Because the approaches of the methods differ and have their own limitations, we
think that a number of verification methods should be applied to a developed
DNN. To apply a number of methods to the DNN, it is necessary to translate
either the implementation of the DNN or the verification method so that one
runs in the same environment as the other. Since those translations are
time-consuming, a utility tool, named DeepSaucer, which helps to retain and
reuse implementations of DNNs, verification methods, and their environments, is
proposed. In DeepSaucer, code snippets of loading DNNs, running verification
methods, and creating their environments are retained and reused as software
assets in order to reduce cost of verifying DNNs. The feasibility of DeepSaucer
is confirmed by implementing it on the basis of Anaconda, which provides
virtual environment for loading a DNN and running a verification method. In
addition, the effectiveness of DeepSaucer is demonstrated by usecase examples.
Imagining an Engineer: On GAN-Based Data Augmentation Perpetuating Biases
Authors: Niharika Jain, Lydia Manikonda, Alberto Olmo Hernandez, Sailik Sengupta, Subbarao Kambhampati
Date: 2018-11-09 00:00:00
Tasks: Data Augmentation

The use of synthetic data generated by Generative Adversarial Networks (GANs)
has become quite a popular method to do data augmentation for many
applications. While practitioners celebrate this as an economical way to get
more synthetic data that can be used to train downstream classifiers, it is not
clear that they recognize the inherent pitfalls of this technique. In this
paper, we aim to exhort practitioners against deriving any false sense of
security against data biases based on data augmentation. To drive this point
home, we show that starting with a dataset consisting of head-shots of
engineering researchers, GAN-based augmentation "imagines" synthetic engineers,
most of whom have masculine features and white skin color (inferred from a
human subject study conducted on Amazon Mechanical Turk). This demonstrates how
biases inherent in the training data are reinforced, and sometimes even
amplified, by GAN-based data augmentation; it should serve as a cautionary tale
for the lay practitioners.