pwc-archive/papers-with-abstracts huggingface dataset:
Energy-based Models for Video Anomaly Detection
Authors: Hung Vu, Dinh Phung, Tu Dinh Nguyen, Anthony Trevors, Svetha Venkatesh
Date: 2017-08-17 00:00:00
Tasks: Anomaly Detection, Feature Engineering, Intrusion Detection, Network Intrusion Detection, Video Anomaly Detection

Automated detection of abnormalities in data has been studied in research
area in recent years because of its diverse applications in practice including
video surveillance, industrial damage detection and network intrusion
detection. However, building an effective anomaly detection system is a
non-trivial task since it requires to tackle challenging issues of the shortage
of annotated data, inability of defining anomaly objects explicitly and the
expensive cost of feature engineering procedure. Unlike existing appoaches
which only partially solve these problems, we develop a unique framework to
cope the problems above simultaneously. Instead of hanlding with ambiguous
definition of anomaly objects, we propose to work with regular patterns whose
unlabeled data is abundant and usually easy to collect in practice. This allows
our system to be trained completely in an unsupervised procedure and liberate
us from the need for costly data annotation. By learning generative model that
capture the normality distribution in data, we can isolate abnormal data points
that result in low normality scores (high abnormality scores). Moreover, by
leverage on the power of generative networks, i.e. energy-based models, we are
also able to learn the feature representation automatically rather than
replying on hand-crafted features that have been dominating anomaly detection
research over many decades. We demonstrate our proposal on the specific
application of video anomaly detection and the experimental results indicate
that our method performs better than baselines and are comparable with
state-of-the-art methods in many benchmark video anomaly detection datasets.
Brain Abnormality Detection by Deep Convolutional Neural Network
Authors: Mina Rezaei, Haojin Yang, Christoph Meinel
Date: 2017-08-17 00:00:00
Tasks: Anomaly Detection, Classification, General Classification

In this paper, we describe our method for classification of brain magnetic
resonance (MR) images into different abnormalities and healthy classes based on
the deep neural network. We propose our method to detect high and low-grade
glioma, multiple sclerosis, and Alzheimer diseases as well as healthy cases.
Our network architecture has ten learning layers that include seven
convolutional layers and three fully connected layers. We have achieved a
promising result in five categories of brain images (classification task) with
95.7% accuracy.
Towards life cycle identification of malaria parasites using machine learning and Riemannian geometry
Authors: Arash Mehrjou
Date: 2017-08-17 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Diagnostic

Malaria is a serious infectious disease that is responsible for over half
million deaths yearly worldwide. The major cause of these mortalities is late
or inaccurate diagnosis. Manual microscopy is currently considered as the
dominant diagnostic method for malaria. However, it is time consuming and prone
to human errors. The aim of this paper is to automate the diagnosis process and
minimize the human intervention. We have developed the hardware and software
for a cost-efficient malaria diagnostic system. This paper describes the
manufactured hardware and also proposes novel software to handle parasite
detection and life-stage identification. A motorized microscope is developed to
take images from Giemsa-stained blood smears. A patch-based unsupervised
statistical clustering algorithm is proposed which offers a novel method for
classification of different regions within blood images. The proposed method
provides better robustness against different imaging settings. The core of the
proposed algorithm is a model called Mixture of Independent Component Analysis.
A manifold based optimization method is proposed that facilitates the
application of the model for high dimensional data usually acquired in medical
microscopy. The method was tested on 600 blood slides with various imaging
conditions. The speed of the method is higher than current supervised systems
while its accuracy is comparable to or better than them.
SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes
Authors: Marc Assens, Kevin McGuinness, Xavier Giro-i-Nieto, Noel E. O'Connor
Date: 2017-07-11 00:00:00
Tasks: Scanpath prediction

We introduce SaltiNet, a deep neural network for scanpath prediction trained
on 360-degree images. The model is based on a temporal-aware novel
representation of saliency information named the saliency volume. The first
part of the network consists of a model trained to generate saliency volumes,
whose parameters are fit by back-propagation computed from a binary cross
entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling
strategies over these volumes are used to generate scanpaths over the
360-degree images. Our experiments show the advantages of using saliency
volumes, and how they can be used for related tasks. Our source code and
trained models available at
https://github.com/massens/saliency-360salient-2017.
A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games
Authors: Felix Leibfried, Nate Kushman, Katja Hofmann
Date: 2016-11-21 00:00:00
Tasks: Atari Games, Model-based Reinforcement Learning, Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning is concerned with identifying reward-maximizing
behaviour policies in environments that are initially unknown. State-of-the-art
reinforcement learning approaches, such as deep Q-networks, are model-free and
learn to act effectively across a wide range of environments such as Atari
games, but require huge amounts of data. Model-based techniques are more
data-efficient, but need to acquire explicit knowledge about the environment.
  In this paper, we take a step towards using model-based techniques in
environments with a high-dimensional visual state space by demonstrating that
it is possible to learn system dynamics and the reward structure jointly. Our
contribution is to extend a recently developed deep neural network for video
frame prediction in Atari games to enable reward prediction as well. To this
end, we phrase a joint optimization problem for minimizing both video frame and
reward reconstruction loss, and adapt network parameters accordingly. Empirical
evaluations on five Atari games demonstrate accurate cumulative reward
prediction of up to 200 frames. We consider these results as opening up
important directions for model-based reinforcement learning in complex,
initially unknown environments.
High Efficient Reconstruction of Single-shot T2 Mapping from OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network
Authors: Congbo Cai, Yiqing Zeng, Chao Wang, Shuhui Cai, Jun Zhang, Zhong Chen, Xinghao Ding, Jianhui Zhong
Date: 2017-08-17 00:00:00

Purpose: An end-to-end deep convolutional neural network (CNN) based on deep
residual network (ResNet) was proposed to efficiently reconstruct reliable T2
mapping from single-shot OverLapping-Echo Detachment (OLED) planar imaging.
Methods: The training dataset was obtained from simulations carried out on
SPROM software developed by our group. The relationship between the original
OLED image containing two echo signals and the corresponded T2 mapping was
learned by ResNet training. After the ResNet was trained, it was applied to
reconstruct the T2 mapping from simulation and in vivo human brain data.
Results: Though the ResNet was trained entirely on simulated data, the trained
network was generalized well to real human brain data. The results from
simulation and in vivo human brain experiments show that the proposed method
significantly outperformed the echo-detachment-based method. Reliable T2
mapping was achieved within tens of milliseconds after the network had been
trained while the echo-detachment-based OLED reconstruction method took
minutes. Conclusion: The proposed method will greatly facilitate real-time
dynamic and quantitative MR imaging via OLED sequence, and ResNet has the
potential to reconstruct images from complex MRI sequence efficiently.
Adaptive Diffusion Schemes for Heterogeneous Networks
Authors: Jesus Fernandez-Bes, Jerónimo Arenas-García, Magno T. M. Silva, Luis A. Azpicueta-Ruiz
Date: 2015-04-08 00:00:00

In this paper, we deal with distributed estimation problems in diffusion
networks with heterogeneous nodes, i.e., nodes that either implement different
adaptive rules or differ in some other aspect such as the filter structure or
length, or step size. Although such heterogeneous networks have been considered
from the first works on diffusion networks, obtaining practical and robust
schemes to adaptively adjust the combiners in different scenarios is still an
open problem. In this paper, we study a diffusion strategy specially designed
and suited to heterogeneous networks. Our approach is based on two key
ingredients: 1) the adaptation and combination phases are completely decoupled,
so that network nodes keep purely local estimations at all times; and 2)
combiners are adapted to minimize estimates of the network mean-square-error.
Our scheme is compared with the standard Adapt-then-Combine scheme and
theoretically analyzed using energy conservation arguments. Several experiments
involving networks with heterogeneous nodes show that the proposed decoupled
Adapt-then-Combine approach with adaptive combiners outperforms other
state-of-the-art techniques, becoming a competitive approach in these
scenarios.
Revisiting revisits in trajectory recommendation
Authors: Aditya Krishna Menon, Dawei Chen, Lexing Xie, Cheng Soon Ong
Date: 2017-08-17 00:00:00

Trajectory recommendation is the problem of recommending a sequence of places
in a city for a tourist to visit. It is strongly desirable for the recommended
sequence to avoid loops, as tourists typically would not wish to revisit the
same location. Given some learned model that scores sequences, how can we then
find the highest-scoring sequence that is loop-free? This paper studies this
problem, with three contributions. First, we detail three distinct approaches
to the problem -- graph-based heuristics, integer linear programming, and list
extensions of the Viterbi algorithm -- and qualitatively summarise their
strengths and weaknesses. Second, we explicate how two ostensibly different
approaches to the list Viterbi algorithm are in fact fundamentally identical.
Third, we conduct experiments on real-world trajectory recommendation datasets
to identify the tradeoffs imposed by each of the three approaches. Overall, our
results indicate that a greedy graph-based heuristic offer excellent
performance and runtime, leading us to recommend its use for removing loops at
prediction time.
Deep Transfer Learning with Joint Adaptation Networks
Authors: Mingsheng Long, Han Zhu, Jian-Min Wang, Michael. I. Jordan
Date: 2016-05-21 00:00:00
Proceeding: ICML 2017 8
Tasks: Multi-Source Unsupervised Domain Adaptation, Transfer Learning

Deep networks have been successfully applied to learn transferable features
for adapting models from a source domain to a different target domain. In this
paper, we present joint adaptation networks (JAN), which learn a transfer
network by aligning the joint distributions of multiple domain-specific layers
across domains based on a joint maximum mean discrepancy (JMMD) criterion.
Adversarial training strategy is adopted to maximize JMMD such that the
distributions of the source and target domains are made more distinguishable.
Learning can be performed by stochastic gradient descent with the gradients
computed by back-propagation in linear-time. Experiments testify that our model
yields state of the art results on standard datasets.
Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection
Authors: Penghang Yin, Shuai Zhang, Yingyong Qi, Jack Xin
Date: 2016-12-19 00:00:00
Tasks: object-detection, Object Detection, Quantization

We present LBW-Net, an efficient optimization based method for quantization
and training of the low bit-width convolutional neural networks (CNNs).
Specifically, we quantize the weights to zero or powers of two by minimizing
the Euclidean distance between full-precision weights and quantized weights
during backpropagation. We characterize the combinatorial nature of the low
bit-width quantization problem. For 2-bit (ternary) CNNs, the quantization of
$N$ weights can be done by an exact formula in $O(N\log N)$ complexity. When
the bit-width is three and above, we further propose a semi-analytical
thresholding scheme with a single free parameter for quantization that is
computationally inexpensive. The free parameter is further determined by
network retraining and object detection tests. LBW-Net has several desirable
advantages over full-precision CNNs, including considerable memory savings,
energy efficiency, and faster deployment. Our experiments on PASCAL VOC dataset
show that compared with its 32-bit floating-point counterpart, the performance
of the 6-bit LBW-Net is nearly lossless in the object detection tasks, and can
even do better in some real world visual scenes, while empirically enjoying
more than 4$\times$ faster deployment.
Natural Language Processing: State of The Art, Current Trends and Challenges
Authors: Diksha Khurana, Aditya Koli, Kiran Khatter, Sukhdev Singh
Date: 2017-08-17 00:00:00
Tasks: Machine Translation, Question Answering, Spam detection, Text Generation, Translation

Natural language processing (NLP) has recently gained much attention for
representing and analysing human language computationally. It has spread its
applications in various fields such as machine translation, email spam
detection, information extraction, summarization, medical, and question
answering etc. The paper distinguishes four phases by discussing different
levels of NLP and components of Natural Language Generation (NLG) followed by
presenting the history and evolution of NLP, state of the art presenting the
various applications of NLP and current trends and challenges.
On Unbounded Delays in Asynchronous Parallel Fixed-Point Algorithms
Authors: Robert Hannah, Wotao Yin
Date: 2016-09-15 00:00:00

The need for scalable numerical solutions has motivated the development of
asynchronous parallel algorithms, where a set of nodes run in parallel with
little or no synchronization, thus computing with delayed information. This
paper studies the convergence of the asynchronous parallel algorithm ARock
under potentially unbounded delays.
  ARock is a general asynchronous algorithm that has many applications. It
parallelizes fixed-point iterations by letting a set of nodes randomly choose
solution coordinates and update them in an asynchronous parallel fashion. ARock
takes some recent asynchronous coordinate descent algorithms as special cases
and gives rise to new asynchronous operator-splitting algorithms. Existing
analysis of ARock assumes the delays to be bounded, and uses this bound to set
a step size that is important to both convergence and efficiency. Other work,
though allowing unbounded delays, imposes strict conditions on the underlying
fixed-point operator, resulting in limited applications.
  In this paper, convergence is established under unbounded delays, which can
be either stochastic or deterministic. The proposed step sizes are more
practical and generally larger than those in the existing work. The step size
adapts to the delay distribution or the current delay being experienced in the
system. New Lyapunov functions, which are the key to analyzing asynchronous
algorithms, are generated to obtain our results. A set of applicable
optimization algorithms with large-scale applications are given, including
machine learning and scientific computing algorithms.
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
Authors: Jae Shin Yoon, Francois Rameau, Junsik Kim, Seokju Lee, Seunghak Shin, In So Kweon
Date: 2017-08-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Feature Compression, Object, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation, Visual Object Tracking

We propose a novel video object segmentation algorithm based on pixel-level
matching using Convolutional Neural Networks (CNN). Our network aims to
distinguish the target area from the background on the basis of the pixel-level
similarity between two object units. The proposed network represents a target
object using features from different depth layers in order to take advantage of
both the spatial details and the category-level semantic information.
Furthermore, we propose a feature compression technique that drastically
reduces the memory requirements while maintaining the capability of feature
representation. Two-stage training (pre-training and fine-tuning) allows our
network to handle any target object regardless of its category (even if the
object's type does not belong to the pre-training data) or of variations in its
appearance through a video sequence. Experiments on large datasets demonstrate
the effectiveness of our model - against related methods - in terms of
accuracy, speed, and stability. Finally, we introduce the transferability of
our network to different domains, such as the infrared data domain.
Geometric Enclosing Networks
Authors: Trung Le, Hung Vu, Tu Dinh Nguyen, Dinh Phung
Date: 2017-08-16 00:00:00

Training model to generate data has increasingly attracted research attention
and become important in modern world applications. We propose in this paper a
new geometry-based optimization approach to address this problem. Orthogonal to
current state-of-the-art density-based approaches, most notably VAE and GAN, we
present a fresh new idea that borrows the principle of minimal enclosing ball
to train a generator G\left(\bz\right) in such a way that both training and
generated data, after being mapped to the feature space, are enclosed in the
same sphere. We develop theory to guarantee that the mapping is bijective so
that its inverse from feature space to data space results in expressive
nonlinear contours to describe the data manifold, hence ensuring data generated
are also lying on the data manifold learned from training data. Our model
enjoys a nice geometric interpretation, hence termed Geometric Enclosing
Networks (GEN), and possesses some key advantages over its rivals, namely
simple and easy-to-control optimization formulation, avoidance of mode
collapsing and efficiently learn data manifold representation in a completely
unsupervised manner. We conducted extensive experiments on synthesis and
real-world datasets to illustrate the behaviors, strength and weakness of our
proposed GEN, in particular its ability to handle multi-modal data and quality
of generated data.
Deep Scene Text Detection with Connected Component Proposals
Authors: Fan Jiang, Zhihui Hao, Xinran Liu
Date: 2017-08-17 00:00:00
Tasks: Scene Text Detection, Scene Understanding, Text Detection

A growing demand for natural-scene text detection has been witnessed by the
computer vision community since text information plays a significant role in
scene understanding and image indexing. Deep neural networks are being used due
to their strong capabilities of pixel-wise classification or word localization,
similar to being used in common vision problems. In this paper, we present a
novel two-task network with integrating bottom and top cues. The first task
aims to predict a pixel-by-pixel labeling and based on which, word proposals
are generated with a canonical connected component analysis. The second task
aims to output a bundle of character candidates used later to verify the word
proposals. The two sub-networks share base convolutional features and moreover,
we present a new loss to strengthen the interaction between them. We evaluate
the proposed network on public benchmark datasets and show it can detect
arbitrary-orientation scene text with a finer output boundary. In ICDAR 2013
text localization task, we achieve the state-of-the-art performance with an
F-score of 0.919 and a much better recall of 0.915.
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization
Authors: Zhiyuan Zha, Xinggan Zhang, Qiong Wang, Lan Tang, Xin Liu
Date: 2017-04-24 00:00:00
Tasks: Compressive Sensing, Dictionary Learning

Patch-based sparse representation modeling has shown great potential in image
compressive sensing (CS) reconstruction. However, this model usually suffers
from some limits, such as dictionary learning with great computational
complexity, neglecting the relationship among similar patches. In this paper, a
group-based sparse representation method with non-convex regularization
(GSR-NCR) for image CS reconstruction is proposed. In GSR-NCR, the local
sparsity and nonlocal self-similarity of images is simultaneously considered in
a unified framework. Different from the previous methods based on
sparsity-promoting convex regularization, we extend the non-convex weighted Lp
(0 < p < 1) penalty function on group sparse coefficients of the data matrix,
rather than conventional L1-based regularization. To reduce the computational
complexity, instead of learning the dictionary with a high computational
complexity from natural images, we learn the principle component analysis (PCA)
based dictionary for each group. Moreover, to make the proposed scheme
tractable and robust, we have developed an efficient iterative
shrinkage/thresholding algorithm to solve the non-convex optimization problem.
Experimental results demonstrate that the proposed method outperforms many
state-of-the-art techniques for image CS reconstruction.
Deep & Cross Network for Ad Click Predictions
Authors: Ruoxi Wang, Bin Fu, Gang Fu, Mingliang Wang
Date: 2017-08-17 00:00:00
Tasks: Click-Through Rate Prediction, Feature Engineering

Feature engineering has been the key to the success of many prediction
models. However, the process is non-trivial and often requires manual feature
engineering or exhaustive searching. DNNs are able to automatically learn
feature interactions; however, they generate all the interactions implicitly,
and are not necessarily efficient in learning all types of cross features. In
this paper, we propose the Deep & Cross Network (DCN) which keeps the benefits
of a DNN model, and beyond that, it introduces a novel cross network that is
more efficient in learning certain bounded-degree feature interactions. In
particular, DCN explicitly applies feature crossing at each layer, requires no
manual feature engineering, and adds negligible extra complexity to the DNN
model. Our experimental results have demonstrated its superiority over the
state-of-art algorithms on the CTR prediction dataset and dense classification
dataset, in terms of both model accuracy and memory usage.