pwc-archive/papers-with-abstracts huggingface dataset:
Path to Stochastic Stability: Comparative Analysis of Stochastic Learning Dynamics in Games
Authors: Hassan Jaleel, Jeff S. Shamma
Date: 2018-04-08 00:00:00
Tasks: Decision Making

Stochastic stability is a popular solution concept for stochastic learning
dynamics in games. However, a critical limitation of this solution concept is
its inability to distinguish between different learning rules that lead to the
same steady-state behavior. We address this limitation for the first time and
develop a framework for the comparative analysis of stochastic learning
dynamics with different update rules but same steady-state behavior. We present
the framework in the context of two learning dynamics: Log-Linear Learning
(LLL) and Metropolis Learning (ML). Although both of these dynamics have the
same stochastically stable states, LLL and ML correspond to different
behavioral models for decision making. Moreover, we demonstrate through an
example setup of sensor coverage game that for each of these dynamics, the
paths to stochastically stable states exhibit distinctive behaviors. Therefore,
we propose multiple criteria to analyze and quantify the differences in the
short and medium run behavior of stochastic learning dynamics. We derive and
compare upper bounds on the expected hitting time to the set of Nash equilibria
for both LLL and ML. For the medium to long-run behavior, we identify a set of
tools from the theory of perturbed Markov chains that result in a hierarchical
decomposition of the state space into collections of states called cycles. We
compare LLL and ML based on the proposed criteria and develop invaluable
insights into the comparative behavior of the two dynamics.
Detecting Multi-Oriented Text with Corner-based Region Proposals
Authors: Linjie Deng, Yanxiang Gong, Yi Lin, Jingwen Shuai, Xiaoguang Tu, Yuefei Zhang, Zheng Ma, Mei Xie
Date: 2018-04-08 00:00:00
Tasks: Data Augmentation, Robust classification, Scene Text Detection, Text Detection

Previous approaches for scene text detection usually rely on manually defined sliding windows. This work presents an intuitive two-stage region-based method to detect multi-oriented text without any prior knowledge regarding the textual shape. In the first stage, we estimate the possible locations of text instances by detecting and linking corners instead of shifting a set of default anchors. The quadrilateral proposals are geometry adaptive, which allows our method to cope with various text aspect ratios and orientations. In the second stage, we design a new pooling layer named Dual-RoI Pooling which embeds data augmentation inside the region-wise subnetwork for more robust classification and regression over these proposals. Experimental results on public benchmarks confirm that the proposed method is capable of achieving comparable performance with state-of-the-art methods. The code is publicly available at https://github.com/xhzdeng/crpn
Complex Network Classification with Convolutional Neural Network
Authors: Ruyue Xin, Jiang Zhang, Yitong Shao
Date: 2018-02-02 00:00:00
Tasks: Classification, Clustering, General Classification, Network Embedding

Classifying large scale networks into several categories and distinguishing
them according to their fine structures is of great importance with several
applications in real life. However, most studies of complex networks focus on
properties of a single network but seldom on classification, clustering, and
comparison between different networks, in which the network is treated as a
whole. Due to the non-Euclidean properties of the data, conventional methods
can hardly be applied on networks directly. In this paper, we propose a novel
framework of complex network classifier (CNC) by integrating network embedding
and convolutional neural network to tackle the problem of network
classification. By training the classifiers on synthetic complex network data
and real international trade network data, we show CNC can not only classify
networks in a high accuracy and robustness, it can also extract the features of
the networks automatically.
Fast Single Image Rain Removal via a Deep Decomposition-Composition Network
Authors: Siyuan LI, Wenqi Ren, Jiawan Zhang, Jinke Yu, Xiaojie Guo
Date: 2018-04-08 00:00:00
Tasks: GPU, Rain Removal

Rain effect in images typically is annoying for many multimedia and computer
vision tasks. For removing rain effect from a single image, deep leaning
techniques have been attracting considerable attentions. This paper designs a
novel multi-task leaning architecture in an end-to-end manner to reduce the
mapping range from input to output and boost the performance. Concretely, a
decomposition net is built to split rain images into clean background and rain
layers. Different from previous architectures, our model consists of, besides a
component representing the desired clean image, an extra component for the rain
layer. During the training phase, we further employ a composition structure to
reproduce the input by the separated clean image and rain information for
improving the quality of decomposition. Experimental results on both synthetic
and real images are conducted to reveal the high-quality recovery by our
design, and show its superiority over other state-of-the-art methods.
Furthermore, our design is also applicable to other layer decomposition tasks
like dust removal. More importantly, our method only requires about 50ms,
significantly faster than the competitors, to process a testing image in VGA
resolution on a GTX 1080 GPU, making it attractive for practical use.
Learning-based Video Motion Magnification
Authors: Tae-Hyun Oh, Ronnachai Jaroensri, Changil Kim, Mohamed Elgharib, Frédo Durand, William T. Freeman, Wojciech Matusik
Date: 2018-04-08 00:00:00
Proceeding: ECCV 2018 9
Tasks: Motion Magnification

Video motion magnification techniques allow us to see small motions
previously invisible to the naked eyes, such as those of vibrating airplane
wings, or swaying buildings under the influence of the wind. Because the motion
is small, the magnification results are prone to noise or excessive blurring.
The state of the art relies on hand-designed filters to extract representations
that may not be optimal. In this paper, we seek to learn the filters directly
from examples using deep convolutional neural networks. To make training
tractable, we carefully design a synthetic dataset that captures small motion
well, and use two-frame input for training. We show that the learned filters
achieve high-quality results on real videos, with less ringing artifacts and
better noise characteristics than previous methods. While our model is not
trained with temporal filters, we found that the temporal filters can be used
with our extracted representations up to a moderate magnification, enabling a
frequency-based motion selection. Finally, we analyze the learned filters and
show that they behave similarly to the derivative filters used in previous
works. Our code, trained model, and datasets will be available online.
Supervised Convolutional Sparse Coding
Authors: Lama Affara, Bernard Ghanem, Peter Wonka
Date: 2018-04-08 00:00:00
Tasks: Image Reconstruction, Image Restoration

Convolutional Sparse Coding (CSC) is a well-established image representation
model especially suited for image restoration tasks. In this work, we extend
the applicability of this model by proposing a supervised approach to
convolutional sparse coding, which aims at learning discriminative dictionaries
instead of purely reconstructive ones. We incorporate a supervised
regularization term into the traditional unsupervised CSC objective to
encourage the final dictionary elements to be discriminative. Experimental
results show that using supervised convolutional learning results in two key
advantages. First, we learn more semantically relevant filters in the
dictionary and second, we achieve improved image reconstruction on unseen data.
Fast Convolutional Sparse Coding in the Dual Domain
Authors: Lama Affara, Bernard Ghanem, Peter Wonka
Date: 2017-09-27 00:00:00
Tasks: Video Compression

Convolutional sparse coding (CSC) is an important building block of many
computer vision applications ranging from image and video compression to deep
learning. We present two contributions to the state of the art in CSC. First,
we significantly speed up the computation by proposing a new optimization
framework that tackles the problem in the dual domain. Second, we extend the
original formulation to higher dimensions in order to process a wider range of
inputs, such as RGB images and videos. Our results show up to 20 times speedup
compared to current state-of-the-art CSC solvers.
Cascaded Pyramid Network for Multi-Person Pose Estimation
Authors: Yilun Chen, Zhicheng Wang, Yuxiang Peng, Zhiqiang Zhang, Gang Yu, Jian Sun
Date: 2017-11-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation

The topic of multi-person pose estimation has been largely improved recently,
especially with the development of convolutional neural network. However, there
still exist a lot of challenging cases, such as occluded keypoints, invisible
keypoints and complex background, which cannot be well addressed. In this
paper, we present a novel network structure called Cascaded Pyramid Network
(CPN) which targets to relieve the problem from these "hard" keypoints. More
specifically, our algorithm includes two stages: GlobalNet and RefineNet.
GlobalNet is a feature pyramid network which can successfully localize the
"simple" keypoints like eyes and hands but may fail to precisely recognize the
occluded or invisible keypoints. Our RefineNet tries explicitly handling the
"hard" keypoints by integrating all levels of feature representations from the
GlobalNet together with an online hard keypoint mining loss. In general, to
address the multi-person pose estimation problem, a top-down pipeline is
adopted to first generate a set of human bounding boxes based on a detector,
followed by our CPN for keypoint localization in each human bounding box. Based
on the proposed algorithm, we achieve state-of-art results on the COCO keypoint
benchmark, with average precision at 73.0 on the COCO test-dev dataset and 72.1
on the COCO test-challenge dataset, which is a 19% relative improvement
compared with 60.5 from the COCO 2016 keypoint challenge.Code
(https://github.com/chenyilun95/tf-cpn.git) and the detection results are
publicly available for further research.
Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB
Authors: Tomoyuki Suzuki, Hirokatsu Kataoka, Yoshimitsu Aoki, Yutaka Satoh
Date: 2018-04-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: Accident Anticipation

In this paper, we propose a novel approach for traffic accident anticipation
through (i) Adaptive Loss for Early Anticipation (AdaLEA) and (ii) a
large-scale self-annotated incident database for anticipation. The proposed
AdaLEA allows a model to gradually learn an earlier anticipation as training
progresses. The loss function adaptively assigns penalty weights depending on
how early the model can an- ticipate a traffic accident at each epoch.
Additionally, we construct a Near-miss Incident DataBase for anticipation. This
database contains an enormous number of traffic near- miss incident videos and
annotations for detail evaluation of two tasks, risk anticipation and
risk-factor anticipation. In our experimental results, we found our proposal
achieved the highest scores for risk anticipation (+6.6% better on mean average
precision (mAP) and 2.36 sec earlier than previous work on the average
time-to-collision (ATTC)) and risk-factor anticipation (+4.3% better on mAP and
0.70 sec earlier than previous work on ATTC).
Qualitative Judgement of Research Impact: Domain Taxonomy as a Fundamental Framework for Judgement of the Quality of Research
Authors: Fionn Murtagh, Michael Orlov, Boris Mirkin
Date: 2016-07-11 00:00:00

The appeal of metric evaluation of research impact has attracted considerable
interest in recent times. Although the public at large and administrative
bodies are much interested in the idea, scientists and other researchers are
much more cautious, insisting that metrics are but an auxiliary instrument to
the qualitative peer-based judgement. The goal of this article is to propose
availing of such a well positioned construct as domain taxonomy as a tool for
directly assessing the scope and quality of research. We first show how
taxonomies can be used to analyse the scope and perspectives of a set of
research projects or papers. Then we proceed to define a research team or
researcher's rank by those nodes in the hierarchy that have been created or
significantly transformed by the results of the researcher. An experimental
test of the approach in the data analysis domain is described. Although the
concept of taxonomy seems rather simplistic to describe all the richness of a
research domain, its changes and use can be made transparent and subject to
open discussions.
Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks
Authors: Shahar Harel, Kira Radinsky
Date: 2018-04-08 00:00:00
Tasks: Diversity, Drug Discovery, valid

Designing a new drug is a lengthy and expensive process. As the space of
potential molecules is very large (10^23-10^60), a common technique during drug
discovery is to start from a molecule which already has some of the desired
properties. An interdisciplinary team of scientists generates hypothesis about
the required changes to the prototype. In this work, we develop an algorithmic
unsupervised-approach that automatically generates potential drug molecules
given a prototype drug. We show that the molecules generated by the system are
valid molecules and significantly different from the prototype drug. Out of the
compounds generated by the system, we identified 35 FDA-approved drugs. As an
example, our system generated Isoniazid - one of the main drugs for
Tuberculosis. The system is currently being deployed for use in collaboration
with pharmaceutical companies to further analyze the additional generated
molecules.
The Monge-Kantorovich Optimal Transport Distance for Image Comparison
Authors: Michael Snow, Jan Van lent
Date: 2018-04-08 00:00:00
Tasks: BIG-bench Machine Learning, Optical Character Recognition, Optical Character Recognition (OCR)

This paper focuses on the Monge-Kantorovich formulation of the optimal
transport problem and the associated $L^2$ Wasserstein distance. We use the
$L^2$ Wasserstein distance in the Nearest Neighbour (NN) machine learning
architecture to demonstrate the potential power of the optimal transport
distance for image comparison. We compare the Wasserstein distance to other
established distances - including the partial differential equation (PDE)
formulation of the optimal transport problem - and demonstrate that on the well
known MNIST optical character recognition dataset, it achieves excellent
results.
Environmental Sound Recognition using Masked Conditional Neural Networks
Authors: Fady Medhat, David Chesmore, John Robinson
Date: 2018-04-08 00:00:00

Neural network based architectures used for sound recognition are usually
adapted from other application domains, which may not harness sound related
properties. The ConditionaL Neural Network (CLNN) is designed to consider the
relational properties across frames in a temporal signal, and its extension the
Masked ConditionaL Neural Network (MCLNN) embeds a filterbank behavior within
the network, which enforces the network to learn in frequency bands rather than
bins. Additionally, it automates the exploration of different feature
combinations analogous to handcrafting the optimum combination of features for
a recognition task. We applied the MCLNN to the environmental sounds of the
ESC-10 dataset. The MCLNN achieved competitive accuracies compared to
state-of-the-art convolutional neural networks and hand-crafted attempts.
Monge's Optimal Transport Distance for Image Classification
Authors: Michael Snow, Jan Van lent
Date: 2016-12-01 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

This paper focuses on a similarity measure, known as the Wasserstein
distance, with which to compare images. The Wasserstein distance results from a
partial differential equation (PDE) formulation of Monge's optimal transport
problem. We present an efficient numerical solution method for solving Monge's
problem. To demonstrate the measure's discriminatory power when comparing
images, we use a $1$-Nearest Neighbour ($1$-NN) machine learning algorithm to
illustrate the measure's potential benefits over other more traditional
distance metrics and also the Tangent Space distance, designed to perform
excellently on the well-known MNIST dataset. To our knowledge, the PDE
formulation of the Wasserstein metric has not been presented for dealing with
image comparison, nor has the Wasserstein distance been used within the
$1$-nearest neighbour architecture.
Emotion Orientated Recommendation System for Hiroshima Tourist by Fuzzy Petri Net
Authors: Takumi Ichimura, Issei Tachibana
Date: 2018-04-08 00:00:00

We developed an Android Smartophone application software for tourist
information system. Especially, the agent system recommends the sightseeing
spot and local hospitality corresponding to the current feelings. The system
such as concierge can estimate user's emotion and mood by Emotion Generating
Calculations and Mental State Transition Network. In this paper, the system
decides the next candidates for spots and foods by the reasoning of fuzzy Petri
Net in order to make more smooth communication between human and smartphone.
The system was developed for Hiroshima Tourist Information and described some
hospitality about the concierge system.
Between-class Learning for Image Classification
Authors: Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Classification, General Classification, image-classification, Image Classification

In this paper, we propose a novel learning method for image classification
called Between-Class learning (BC learning). We generate between-class images
by mixing two images belonging to different classes with a random ratio. We
then input the mixed image to the model and train the model to output the
mixing ratio. BC learning has the ability to impose constraints on the shape of
the feature distributions, and thus the generalization ability is improved. BC
learning is originally a method developed for sounds, which can be digitally
mixed. Mixing two image data does not appear to make sense; however, we argue
that because convolutional neural networks have an aspect of treating input
data as waveforms, what works on sounds must also work on images. First, we
propose a simple mixing method using internal divisions, which surprisingly
proves to significantly improve performance. Second, we propose a mixing method
that treats the images as waveforms, which leads to a further improvement in
performance. As a result, we achieved 19.4% and 2.26% top-1 errors on
ImageNet-1K and CIFAR-10, respectively.
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
Authors: Zhen Yang, Wei Chen, Feng Wang, Bo Xu
Date: 2017-03-15 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, NMT, Sentence, Translation

This paper proposes an approach for applying GANs to NMT. We build a
conditional sequence generative adversarial net which comprises of two
adversarial sub models, a generator and a discriminator. The generator aims to
generate sentences which are hard to be discriminated from human-translated
sentences (i.e., the golden target sentences), And the discriminator makes
efforts to discriminate the machine-generated sentences from human-translated
ones. The two sub models play a mini-max game and achieve the win-win situation
when they reach a Nash Equilibrium. Additionally, the static sentence-level
BLEU is utilized as the reinforced objective for the generator, which biases
the generation towards high BLEU points. During training, both the dynamic
discriminator and the static BLEU objective are employed to evaluate the
generated sentences and feedback the evaluations to guide the learning of the
generator. Experimental results show that the proposed model consistently
outperforms the traditional RNNSearch and the newly emerged state-of-the-art
Transformer on English-German and Chinese-English translation tasks.
A Clonal Selection Algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM
Authors: Takumi Ichimura, Shin Kamada
Date: 2018-04-08 00:00:00

Mobile Phone based Participatory Sensing (MPPS) system involves a community
of users sending personal information and participating in autonomous sensing
through their mobile phones. Sensed data can also be obtained from external
sensing devices that can communicate wirelessly to the phone. Our developed
tourist subjective data collection system with Android smartphone can determine
the filtering rules to provide the important information of sightseeing spot.
The rules are automatically generated by Interactive Growing Hierarchical SOM.
However, the filtering rules related to photograph were not generated, because
the extraction of the specified characteristics from images cannot be realized.
We propose the effective method of the Levenshtein distance to deduce the
spatial proximity of image viewpoints and thus determine the specified pattern
in which images should be processed. To verify the proposed method, some
experiments to classify the subjective data with images are executed by
Interactive GHSOM and Clonal Selection Algorithm with Immunological Memory
Cells in this paper.
OATM: Occlusion Aware Template Matching by Consensus Set Maximization
Authors: Simon Korman, Mark Milam, Stefano Soatto
Date: 2018-04-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: Template Matching

We present a novel approach to template matching that is efficient, can
handle partial occlusions, and comes with provable performance guarantees. A
key component of the method is a reduction that transforms the problem of
searching a nearest neighbor among $N$ high-dimensional vectors, to searching
neighbors among two sets of order $\sqrt{N}$ vectors, which can be found
efficiently using range search techniques. This allows for a quadratic
improvement in search complexity, and makes the method scalable in handling
large search spaces. The second contribution is a hashing scheme based on
consensus set maximization, which allows us to handle occlusions. The resulting
scheme can be seen as a randomized hypothesize-and-test algorithm, which is
equipped with guarantees regarding the number of iterations required for
obtaining an optimal solution with high probability. The predicted matching
rates are validated empirically and the algorithm shows a significant
improvement over the state-of-the-art in both speed and robustness to
occlusions.