pwc-archive/papers-with-abstracts huggingface dataset:
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
Authors: Bruno Ferrarini, Shoaib Ehsan, Ales Leonardis, Naveed Ur Rehman, Klaus D. McDonald-Maier
Date: 2017-09-24 00:00:00

Selecting the most suitable local invariant feature detector for a particular
application has rendered the task of evaluating feature detectors a critical
issue in vision research. Although the literature offers a variety of
comparison works focusing on performance evaluation of image feature detectors
under several types of image transformations, the influence of the scene
content on the performance of local feature detectors has received little
attention so far. This paper aims to bridge this gap with a new framework for
determining the type of scenes which maximize and minimize the performance of
detectors in terms of repeatability rate. The results are presented for several
state-of-the-art feature detectors that have been obtained using a large image
database of 20482 images under JPEG compression, uniform light and blur changes
with 539 different scenes captured from real-world scenarios. These results
provide new insights into the behavior of feature detectors.
Real time ridge orientation estimation for fingerprint images
Authors: Eman Alibeigi, Shadrokh Samavi, Shahram Shirani, Zahra Rahmani
Date: 2017-10-13 00:00:00
Tasks: fingerprint verification

Fingerprint verification is an important bio-metric technique for personal
identification. Most of the automatic verification systems are based on
matching of fingerprint minutiae. Extraction of minutiae is an essential
process which requires estimation of orientation of the lines in an image. Most
of the existing methods involve intense mathematical computations and hence are
performed through software means. In this paper a hardware scheme to perform
real time orientation estimation is presented which is based on pipelined
architecture. Synthesized circuits proved the functionality and accuracy of the
suggested method.
Object Classification in Images of Neoclassical Artifacts Using Deep Learning
Authors: Bernhard Bermeitinger, Maria Christoforaki, Simon Donig, Siegfried Handschuh
Date: 2017-10-13 00:00:00
Tasks: Classification, Deep Learning, General Classification

In this paper, we report on our efforts for using Deep Learning for
classifying artifacts and their features in digital visuals as a part of the
Neoclassica framework. It was conceived to provide scholars with new methods
for analyzing and classifying artifacts and aesthetic forms from the era of
Classicism. The framework accommodates both traditional knowledge
representation as a formal ontology and data-driven knowledge discovery, where
cultural patterns will be identified by means of algorithms in statistical
analysis and machine learning. We created a Deep Learning approach trained on
photographs to classify the objects inside these photographs. In a next step,
we will apply a different Deep Learning approach. It is capable of locating
multiple objects inside an image and classifying them with a high accuracy.
Distributional Modeling on a Diet: One-shot Word Learning from Text Only
Authors: Su Wang, Stephen Roller, Katrin Erk
Date: 2017-04-14 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: One-Shot Learning

We test whether distributional models can do one-shot learning of
definitional properties from text only. Using Bayesian models, we find that
first learning overarching structure in the known data, regularities in textual
contexts and in properties, helps one-shot learning, and that individual
context items can be highly informative. Our experiments show that our model
can learn properties from a single exposure when given an informative
utterance.
Two-stage Algorithm for Fairness-aware Machine Learning
Authors: Junpei Komiyama, Hajime Shimao
Date: 2017-10-13 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Fairness, General Classification, regression, Vocal Bursts Valence Prediction

Algorithmic decision making process now affects many aspects of our lives.
Standard tools for machine learning, such as classification and regression, are
subject to the bias in data, and thus direct application of such off-the-shelf
tools could lead to a specific group being unfairly discriminated. Removing
sensitive attributes of data does not solve this problem because a
\textit{disparate impact} can arise when non-sensitive attributes and sensitive
attributes are correlated. Here, we study a fair machine learning algorithm
that avoids such a disparate impact when making a decision. Inspired by the
two-stage least squares method that is widely used in the field of economics,
we propose a two-stage algorithm that removes bias in the training data. The
proposed algorithm is conceptually simple. Unlike most of existing fair
algorithms that are designed for classification tasks, the proposed method is
able to (i) deal with regression tasks, (ii) combine explanatory attributes to
remove reverse discrimination, and (iii) deal with numerical sensitive
attributes. The performance and fairness of the proposed algorithm are
evaluated in simulations with synthetic and real-world datasets.
A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes
Authors: Grzegorz Dudek
Date: 2017-10-13 00:00:00

Neural networks with random hidden nodes have gained increasing interest from
researchers and practical applications. This is due to their unique features
such as very fast training and universal approximation property. In these
networks the weights and biases of hidden nodes determining the nonlinear
feature mapping are set randomly and are not learned. Appropriate selection of
the intervals from which weights and biases are selected is extremely
important. This topic has not yet been sufficiently explored in the literature.
In this work a method of generating random weights and biases is proposed. This
method generates the parameters of the hidden nodes in such a way that
nonlinear fragments of the activation functions are located in the input space
regions with data and can be used to construct the surface approximating a
nonlinear target function. The weights and biases are dependent on the input
data range and activation function type. The proposed methods allows us to
control the generalization degree of the model. These all lead to improvement
in approximation performance of the network. Several experiments show very
promising results.
Manifold regularization based on Nystr{ö}m type subsampling
Authors: Abhishake Rastogi, Sivananthan Sampath
Date: 2017-10-13 00:00:00
Tasks: image-classification, Image Classification, Intrusion Detection, Multi-Task Learning, Vocal Bursts Type Prediction

In this paper, we study the Nystr{\"o}m type subsampling for large scale
kernel methods to reduce the computational complexities of big data. We discuss
the multi-penalty regularization scheme based on Nystr{\"o}m type subsampling
which is motivated from well-studied manifold regularization schemes. We
develop a theoretical analysis of multi-penalty least-square regularization
scheme under the general source condition in vector-valued function setting,
therefore the results can also be applied to multi-task learning problems. We
achieve the optimal minimax convergence rates of multi-penalty regularization
using the concept of effective dimension for the appropriate subsampling size.
We discuss an aggregation approach based on linear function strategy to combine
various Nystr{\"o}m approximants. Finally, we demonstrate the performance of
multi-penalty regularization based on Nystr{\"o}m type subsampling on
Caltech-101 data set for multi-class image classification and NSL-KDD benchmark
data set for intrusion detection problem.
TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization
Authors: Dominik Marek Loroch, Norbert Wehn, Franz-Josef Pfreundt, Janis Keuper
Date: 2017-10-13 00:00:00
Tasks: Quantization

Recent research implies that training and inference of deep neural networks
(DNN) can be computed with low precision numerical representations of the
training/test data, weights and gradients without a general loss in accuracy.
The benefit of such compact representations is twofold: they allow a
significant reduction of the communication bottleneck in distributed DNN
training and faster neural network implementations on hardware accelerators
like FPGAs. Several quantization methods have been proposed to map the original
32-bit floating point problem to low-bit representations. While most related
publications validate the proposed approach on a single DNN topology, it
appears to be evident, that the optimal choice of the quantization method and
number of coding bits is topology dependent. To this end, there is no general
theory available, which would allow users to derive the optimal quantization
during the design of a DNN topology. In this paper, we present a quantization
tool box for the TensorFlow framework. TensorQuant allows a transparent
quantization simulation of existing DNN topologies during training and
inference. TensorQuant supports generic quantization methods and allows
experimental evaluation of the impact of the quantization on single layers as
well as on the full topology. In a first series of experiments with
TensorQuant, we show an analysis of fix-point quantizations of popular CNN
topologies.
Efficient Computation in Adaptive Artificial Spiking Neural Networks
Authors: Davide Zambrano, Roeland Nusselder, H. Steven Scholte, Sander Bohte
Date: 2017-10-13 00:00:00

Artificial Neural Networks (ANNs) are bio-inspired models of neural
computation that have proven highly effective. Still, ANNs lack a natural
notion of time, and neural units in ANNs exchange analog values in a
frame-based manner, a computationally and energetically inefficient form of
communication. This contrasts sharply with biological neurons that communicate
sparingly and efficiently using binary spikes. While artificial Spiking Neural
Networks (SNNs) can be constructed by replacing the units of an ANN with
spiking neurons, the current performance is far from that of deep ANNs on hard
benchmarks and these SNNs use much higher firing rates compared to their
biological counterparts, limiting their efficiency. Here we show how spiking
neurons that employ an efficient form of neural coding can be used to construct
SNNs that match high-performance ANNs and exceed state-of-the-art in SNNs on
important benchmarks, while requiring much lower average firing rates. For
this, we use spike-time coding based on the firing rate limiting adaptation
phenomenon observed in biological spiking neurons. This phenomenon can be
captured in adapting spiking neuron models, for which we derive the effective
transfer function. Neural units in ANNs trained with this transfer function can
be substituted directly with adaptive spiking neurons, and the resulting
Adaptive SNNs (AdSNNs) can carry out inference in deep neural networks using up
to an order of magnitude fewer spikes compared to previous SNNs. Adaptive
spike-time coding additionally allows for the dynamic control of neural coding
precision: we show how a simple model of arousal in AdSNNs further halves the
average required firing rate and this notion naturally extends to other forms
of attention. AdSNNs thus hold promise as a novel and efficient model for
neural computation that naturally fits to temporally continuous and
asynchronous applications.
Recent Advances in Zero-shot Recognition
Authors: Yanwei Fu, Tao Xiang, Yu-Gang Jiang, xiangyang xue, Leonid Sigal, Shaogang Gong
Date: 2017-10-13 00:00:00
Tasks: Open Set Learning, Zero-Shot Learning

With the recent renaissance of deep convolution neural networks, encouraging
breakthroughs have been achieved on the supervised recognition tasks, where
each class has sufficient training data and fully annotated training data.
However, to scale the recognition to a large number of classes with few or now
training samples for each class remains an unsolved problem. One approach to
scaling up the recognition is to develop models capable of recognizing unseen
categories without any training instances, or zero-shot recognition/ learning.
This article provides a comprehensive review of existing zero-shot recognition
techniques covering various aspects ranging from representations of models, and
from datasets and evaluation settings. We also overview related recognition
tasks including one-shot and open set recognition which can be used as natural
extensions of zero-shot recognition when limited number of class samples become
available or when zero-shot recognition is implemented in a real-world setting.
Importantly, we highlight the limitations of existing approaches and point out
future research directions in this existing new research area.
Multi-frame image super-resolution with fast upscaling technique
Authors: Longguang Wang, Zaiping Lin, Xinpu Deng, Wei An
Date: 2017-06-20 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Multi-frame image super-resolution (MISR) aims to fuse information in
low-resolution (LR) image sequence to compose a high-resolution (HR) one, which
is applied extensively in many areas recently. Different with single image
super-resolution (SISR), sub-pixel transitions between multiple frames
introduce additional information, attaching more significance to fusion
operator to alleviate the ill-posedness of MISR. For reconstruction-based
approaches, the inevitable projection of reconstruction errors from LR space to
HR space is commonly tackled by an interpolation operator, however crude
interpolation may not fit the natural image and generate annoying blurring
artifacts, especially after fusion operator. In this paper, we propose an
end-to-end fast upscaling technique to replace the interpolation operator,
design upscaling filters in LR space for periodic sub-locations respectively
and shuffle the filter results to derive the final reconstruction errors in HR
space. The proposed fast upscaling technique not only reduce the computational
complexity of the upscaling operation by utilizing shuffling operation to avoid
complex operation in HR space, but also realize superior performance with fewer
blurring artifacts. Extensive experimental results demonstrate the
effectiveness and efficiency of the proposed technique, whilst, combining the
proposed technique with bilateral total variation (BTV) regu-larization, the
MISR approach outperforms state-of-the-art methods.
Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
Authors: Kenji Enomoto, Ken Sakurada, Weimin WANG, Hiroshi Fukui, Masashi Matsuoka, Ryosuke Nakamura, Nobuo Kawaguchi
Date: 2017-10-13 00:00:00
Tasks: Cloud Removal

In this paper, we propose a method for cloud removal from visible light RGB
satellite images by extending the conditional Generative Adversarial Networks
(cGANs) from RGB images to multispectral images. Satellite images have been
widely utilized for various purposes, such as natural environment monitoring
(pollution, forest or rivers), transportation improvement and prompt emergency
response to disasters. However, the obscurity caused by clouds makes it
unstable to monitor the situation on the ground with the visible light camera.
Images captured by a longer wavelength are introduced to reduce the effects of
clouds. Synthetic Aperture Radar (SAR) is such an example that improves
visibility even the clouds exist. On the other hand, the spatial resolution
decreases as the wavelength increases. Furthermore, the images captured by long
wavelengths differs considerably from those captured by visible light in terms
of their appearance. Therefore, we propose a network that can remove clouds and
generate visible light images from the multispectral images taken as inputs.
This is achieved by extending the input channels of cGANs to be compatible with
multispectral images. The networks are trained to output images that are close
to the ground truth using the images synthesized with clouds over the ground
truth as inputs. In the available dataset, the proportion of images of the
forest or the sea is very high, which will introduce bias in the training
dataset if uniformly sampled from the original dataset. Thus, we utilize the
t-Distributed Stochastic Neighbor Embedding (t-SNE) to improve the problem of
bias in the training dataset. Finally, we confirm the feasibility of the
proposed network on the dataset of four bands images, which include three
visible light bands and one near-infrared (NIR) band.
WeText: Scene Text Detection under Weak Supervision
Authors: Shangxuan Tian, Shijian Lu, Chongshou Li
Date: 2017-10-13 00:00:00
Proceeding: ICCV 2017 10
Tasks: Scene Text Detection, Text Detection, Weakly-supervised Learning

The requiring of large amounts of annotated training data has become a common
constraint on various deep learning systems. In this paper, we propose a weakly
supervised scene text detection method (WeText) that trains robust and accurate
scene text detection models by learning from unannotated or weakly annotated
data. With a "light" supervised model trained on a small fully annotated
dataset, we explore semi-supervised and weakly supervised learning on a large
unannotated dataset and a large weakly annotated dataset, respectively. For the
unsupervised learning, the light supervised model is applied to the unannotated
dataset to search for more character training samples, which are further
combined with the small annotated dataset to retrain a superior character
detection model. For the weakly supervised learning, the character searching is
guided by high-level annotations of words/text lines that are widely available
and also much easier to prepare. In addition, we design an unified scene
character detector by adapting regression based deep networks, which greatly
relieves the error accumulation issue that widely exists in most traditional
approaches. Extensive experiments across different unannotated and weakly
annotated datasets show that the scene text detection performance can be
clearly boosted under both scenarios, where the weakly supervised learning can
achieve the state-of-the-art performance by using only 229 fully annotated
scene text images.
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning
Authors: Kyungjae Lee, Sungjoon Choi, Songhwai Oh
Date: 2017-09-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, a sparse Markov decision process (MDP) with novel causal
sparse Tsallis entropy regularization is proposed.The proposed policy
regularization induces a sparse and multi-modal optimal policy distribution of
a sparse MDP. The full mathematical analysis of the proposed sparse MDP is
provided.We first analyze the optimality condition of a sparse MDP. Then, we
propose a sparse value iteration method which solves a sparse MDP and then
prove the convergence and optimality of sparse value iteration using the Banach
fixed point theorem. The proposed sparse MDP is compared to soft MDPs which
utilize causal entropy regularization. We show that the performance error of a
sparse MDP has a constant bound, while the error of a soft MDP increases
logarithmically with respect to the number of actions, where this performance
error is caused by the introduced regularization term. In experiments, we apply
sparse MDPs to reinforcement learning problems. The proposed method outperforms
existing methods in terms of the convergence speed and performance.
Deep Regression Bayesian Network and Its Applications
Authors: Siqi Nie, Meng Zheng, Qiang Ji
Date: 2017-10-13 00:00:00
Tasks: regression

Deep directed generative models have attracted much attention recently due to
their generative modeling nature and powerful data representation ability. In
this paper, we review different structures of deep directed generative models
and the learning and inference algorithms associated with the structures. We
focus on a specific structure that consists of layers of Bayesian Networks due
to the property of capturing inherent and rich dependencies among latent
variables. The major difficulty of learning and inference with deep directed
models with many latent variables is the intractable inference due to the
dependencies among the latent variables and the exponential number of latent
variable configurations. Current solutions use variational methods often
through an auxiliary network to approximate the posterior probability
inference. In contrast, inference can also be performed directly without using
any auxiliary network to maximally preserve the dependencies among the latent
variables. Specifically, by exploiting the sparse representation with the
latent space, max-max instead of max-sum operation can be used to overcome the
exponential number of latent configurations. Furthermore, the max-max operation
and augmented coordinate ascent are applied to both supervised and unsupervised
learning as well as to various inference. Quantitative evaluations on benchmark
datasets of different models are given for both data representation and feature
learning tasks.
Combinatorial Multi-armed Bandits for Real-Time Strategy Games
Authors: Santiago Ontañón
Date: 2017-10-13 00:00:00
Tasks: Multi-Armed Bandits, Real-Time Strategy Games

Games with large branching factors pose a significant challenge for game tree
search algorithms. In this paper, we address this problem with a sampling
strategy for Monte Carlo Tree Search (MCTS) algorithms called {\em na\"{i}ve
sampling}, based on a variant of the Multi-armed Bandit problem called {\em
Combinatorial Multi-armed Bandits} (CMAB). We analyze the theoretical
properties of several variants of {\em na\"{i}ve sampling}, and empirically
compare it against the other existing strategies in the literature for CMABs.
We then evaluate these strategies in the context of real-time strategy (RTS)
games, a genre of computer games characterized by their very large branching
factors. Our results show that as the branching factor grows, {\em na\"{i}ve
sampling} outperforms the other sampling strategies.
VGR-Net: A View Invariant Gait Recognition Network
Authors: Daksh Thapar, Divyansh Aggarwal, Punjal Agarwal, Aditya Nigam
Date: 2017-10-13 00:00:00
Tasks: Gait Recognition, Person Identification

Biometric identification systems have become immensely popular and important
because of their high reliability and efficiency. However person identification
at a distance, still remains a challenging problem. Gait can be seen as an
essential biometric feature for human recognition and identification. It can be
easily acquired from a distance and does not require any user cooperation thus
making it suitable for surveillance. But the task of recognizing an individual
using gait can be adversely affected by varying view points making this task
more and more challenging. Our proposed approach tackles this problem by
identifying spatio-temporal features and performing extensive experimentation
and training mechanism. In this paper, we propose a 3-D Convolution Deep Neural
Network for person identification using gait under multiple view. It is a
2-stage network, in which we have a classification network that initially
identifies the viewing point angle. After that another set of networks (one for
each angle) has been trained to identify the person under a particular viewing
angle. We have tested this network over CASIA-B publicly available database and
have achieved state-of-the-art results. The proposed system is much more
efficient in terms of time and space and performing better for almost all
angles.