pwc-archive/papers-with-abstracts huggingface dataset:
Recent Developments from Attribute Profiles for Remote Sensing Image Classification
Authors: Minh-Tan Pham, Sébastien Lefèvre, Erchan Aptoula, Lorenzo Bruzzone
Date: 2018-03-27 00:00:00
Tasks: Attribute, Classification, General Classification, image-classification, Image Classification, Remote Sensing Image Classification

Morphological attribute profiles (APs) are among the most effective methods
to model the spatial and contextual information for the analysis of remote
sensing images, especially for classification task. Since their first
introduction to this field in early 2010's, many research studies have been
contributed not only to exploit and adapt their use to different applications,
but also to extend and improve their performance for better dealing with more
complex data. In this paper, we revisit and discuss different developments and
extensions from APs which have drawn significant attention from researchers in
the past few years. These studies are analyzed and gathered based on the
concept of multi-stage AP construction. In our experiments, a comparative study
on classification results of two remote sensing data is provided in order to
show their significant improvements compared to the originally proposed APs.
Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation
Authors: Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, ASM Shihavuddin, Bartosz Krawczyk, Francisco Herrera
Date: 2018-03-27 00:00:00
Tasks: Data Augmentation, General Classification, Transfer Learning

The recognition of coral species based on underwater texture images pose a
significant difficulty for machine learning algorithms, due to the three
following challenges embedded in the nature of this data: 1) datasets do not
include information about the global structure of the coral; 2) several species
of coral have very similar characteristics; and 3) defining the spatial borders
between classes is difficult as many corals tend to appear together in groups.
For this reason, the classification of coral species has always required an aid
from a domain expert. The objective of this paper is to develop an accurate
classification model for coral texture images. Current datasets contain a large
number of imbalanced classes, while the images are subject to inter-class
variation. We have analyzed 1) several Convolutional Neural Network (CNN)
architectures, 2) data augmentation techniques and 3) transfer learning. We
have achieved the state-of-the art accuracies using different variations of
ResNet on the two current coral texture datasets, EILAT and RSMAS.
Deep Extreme Cut: From Extreme Points to Object Segmentation
Authors: Kevis-Kokitsi Maninis, Sergi Caelles, Jordi Pont-Tuset, Luc van Gool
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Instance Segmentation, Interactive Segmentation, Object, Segmentation, Semantic Segmentation, Video Object Segmentation

This paper explores the use of extreme points in an object (left-most,
right-most, top, bottom pixels) as input to obtain precise object segmentation
for images and videos. We do so by adding an extra channel to the image in the
input of a convolutional neural network (CNN), which contains a Gaussian
centered in each of the extreme points. The CNN learns to transform this
information into a segmentation of an object that matches those extreme points.
We demonstrate the usefulness of this approach for guided segmentation
(grabcut-style), interactive segmentation, video object segmentation, and dense
segmentation annotation. We show that we obtain the most precise results to
date, also with less user input, in an extensive and varied selection of
benchmarks and datasets. All our models and code are publicly available on
http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr/.
On Generation of Adversarial Examples using Convex Programming
Authors: Emilio Rafael Balda, Arash Behboodi, Rudolf Mathar
Date: 2018-03-09 00:00:00
Tasks: General Classification

It has been observed that deep learning architectures tend to make erroneous
decisions with high reliability for particularly designed adversarial
instances. In this work, we show that the perturbation analysis of these
architectures provides a framework for generating adversarial instances by
convex programming which, for classification tasks, is able to recover variants
of existing non-adaptive adversarial methods. The proposed framework can be
used for the design of adversarial noise under various desirable constraints
and different types of networks. Moreover, this framework is capable of
explaining various existing adversarial methods and can be used to derive new
algorithms as well. We make use of these results to obtain novel algorithms.
The experiments show the competitive performance of the obtained solutions, in
terms of fooling ratio, when benchmarked with well-known adversarial methods.
Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA
Authors: Matthias S. Treder
Date: 2018-03-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Least-squares models such as linear regression and Linear Discriminant
Analysis (LDA) are amongst the most popular statistical learning techniques.
However, since their computation time increases cubically with the number of
features, they are inefficient in high-dimensional neuroimaging datasets.
Fortunately, for k-fold cross-validation, an analytical approach has been
developed that yields the exact cross-validated predictions in least-squares
models without explicitly training the model. Its computation time grows with
the number of test samples. Here, this approach is systematically investigated
in the context of cross-validation and permutation testing. LDA is used
exemplarily but results hold for all other least-squares methods. Furthermore,
a non-trivial extension to multi-class LDA is formally derived. The analytical
approach is evaluated using complexity calculations, simulations, and
permutation testing of an EEG/MEG dataset. Depending on the ratio between
features and samples, the analytical approach is up to 10,000x faster than the
standard approach (retraining the model on each training set). This allows for
a fast cross-validation of least-squares models and multi-class LDA in
high-dimensional data, with obvious applications in multi-dimensional datasets,
Representational Similarity Analysis, and permutation testing.
Saliency Preservation in Low-Resolution Grayscale Images
Authors: Shivanthan A. C. Yohanandan, Adrian G. Dyer, DaCheng Tao, Andy Song
Date: 2017-12-06 00:00:00
Proceeding: ECCV 2018 9

Visual salience detection originated over 500 million years ago and is one of
nature's most efficient mechanisms. In contrast, many state-of-the-art
computational saliency models are complex and inefficient. Most saliency models
process high-resolution color (HC) images; however, insights into the
evolutionary origins of visual salience detection suggest that achromatic
low-resolution vision is essential to its speed and efficiency. Previous
studies showed that low-resolution color and high-resolution grayscale images
preserve saliency information. However, to our knowledge, no one has
investigated whether saliency is preserved in low-resolution grayscale (LG)
images. In this study, we explain the biological and computational motivation
for LG, and show, through a range of human eye-tracking and computational
modeling experiments, that saliency information is preserved in LG images.
Moreover, we show that using LG images leads to significant speedups in model
training and detection times and conclude by proposing LG images for fast and
efficient salience detection.
Learning to Compare: Relation Network for Few-Shot Learning
Authors: Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, Timothy M. Hospedales
Date: 2017-11-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Few-Shot Image Classification, Few-Shot Learning, Meta-Learning, Relation, Relation Network, Zero-Shot Learning

We present a conceptually simple, flexible, and general framework for
few-shot learning, where a classifier must learn to recognise new classes given
only few examples from each. Our method, called the Relation Network (RN), is
trained end-to-end from scratch. During meta-learning, it learns to learn a
deep distance metric to compare a small number of images within episodes, each
of which is designed to simulate the few-shot setting. Once trained, a RN is
able to classify images of new classes by computing relation scores between
query images and the few examples of each new class without further updating
the network. Besides providing improved performance on few-shot learning, our
framework is easily extended to zero-shot learning. Extensive experiments on
five benchmarks demonstrate that our simple approach provides a unified and
effective approach for both of these two tasks.
The 2018 DAVIS Challenge on Video Object Segmentation
Authors: Sergi Caelles, Alberto Montes, Kevis-Kokitsi Maninis, Yu-Hua Chen, Luc van Gool, Federico Perazzi, Jordi Pont-Tuset
Date: 2018-03-01 00:00:00
Tasks: Interactive Segmentation, Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

We present the 2018 DAVIS Challenge on Video Object Segmentation, a public
competition specifically designed for the task of video object segmentation. It
builds upon the DAVIS 2017 dataset, which was presented in the previous edition
of the DAVIS Challenge, and added 100 videos with multiple objects per sequence
to the original DAVIS 2016 dataset. Motivated by the analysis of the results of
the 2017 edition, the main track of the competition will be the same than in
the previous edition (segmentation given the full mask of the objects in the
first frame -- semi-supervised scenario). This edition, however, also adds an
interactive segmentation teaser track, where the participants will interact
with a web service simulating the input of a human that provides scribbles to
iteratively improve the result.
Applications of Artificial Intelligence to Network Security
Authors: Alberto Perez Veiga
Date: 2018-03-27 00:00:00

Attacks to networks are becoming more complex and sophisticated every day.
Beyond the so-called script-kiddies and hacking newbies, there is a myriad of
professional attackers seeking to make serious profits infiltrating in
corporate networks. Either hostile governments, big corporations or mafias are
constantly increasing their resources and skills in cybercrime in order to spy,
steal or cause damage more effectively. traditional approaches to Network
Security seem to start hitting their limits and it is being recognized the need
for a smarter approach to threat detections. This paper provides an
introduction on the need for evolution of Cyber Security techniques and how
Artificial Intelligence could be of application to help solving some of the
problems. It provides also, a high-level overview of some state of the art AI
Network Security techniques, to finish analysing what is the foreseeable future
of the application of AI to Network Security.
Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries
Authors: Pierre Dellenbach, Aurélien Bellet, Jan Ramon
Date: 2018-03-27 00:00:00

The amount of personal data collected in our everyday interactions with
connected devices offers great opportunities for innovative services fueled by
machine learning, as well as raises serious concerns for the privacy of
individuals. In this paper, we propose a massively distributed protocol for a
large set of users to privately compute averages over their joint data, which
can then be used to learn predictive models. Our protocol can find a solution
of arbitrary accuracy, does not rely on a third party and preserves the privacy
of users throughout the execution in both the honest-but-curious and malicious
adversary models. Specifically, we prove that the information observed by the
adversary (the set of maliciours users) does not significantly reduce the
uncertainty in its prediction of private values compared to its prior belief.
The level of privacy protection depends on a quantity related to the Laplacian
matrix of the network graph and generally improves with the size of the graph.
Furthermore, we design a verification procedure which offers protection against
malicious users joining the service with the goal of manipulating the outcome
of the algorithm.
Causal Modeling of Dynamical Systems
Authors: Stephan Bongers, Tineke Blom, Joris M. Mooij
Date: 2018-03-23 00:00:00

Dynamical systems are widely used in science and engineering to model systems consisting of several interacting components. Often, they can be given a causal interpretation in the sense that they not only model the evolution of the states of the system's components over time, but also describe how their evolution is affected by external interventions on the system that perturb the dynamics. We introduce the formal framework of structural dynamical causal models (SDCMs) that explicates the causal semantics of the system's components as part of the model. SDCMs represent a dynamical system as a collection of stochastic processes and specify the basic causal mechanisms that govern the dynamics of each component as a structured system of random differential equations of arbitrary order. SDCMs extend the versatile causal modeling framework of structural causal models (SCMs), also known as structural equation models (SEMs), by explicitly allowing for time-dependence. An SDCM can be thought of as the stochastic-process version of an SCM, where the static random variables of the SCM are replaced by dynamic stochastic processes and their derivatives. We provide the foundations for a theory of SDCMs, by (i) formally defining SDCMs, their solutions, stochastic interventions, and a graphical representation; (ii) studying existence and uniqueness of the solutions for given initial conditions; (iii) providing Markov properties for SDCMs with initial conditions; (iv) discussing under which conditions SDCMs equilibrate to SCMs as time tends to infinity; (v) relating the properties of the SDCM to those of the equilibrium SCM. This correspondence enables one to leverage the wealth of statistical tools and discovery methods available for SCMs when studying the causal semantics of a large class of stochastic dynamical systems. The theory is illustrated with examples from different scientific domains.
Reinforcement Learning for Fair Dynamic Pricing
Authors: Roberto Maestre, Juan Duque, Alberto Rubio, Juan Arévalo
Date: 2018-03-27 00:00:00
Tasks: Fairness, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Unfair pricing policies have been shown to be one of the most negative
perceptions customers can have concerning pricing, and may result in long-term
losses for a company. Despite the fact that dynamic pricing models help
companies maximize revenue, fairness and equality should be taken into account
in order to avoid unfair price differences between groups of customers. This
paper shows how to solve dynamic pricing by using Reinforcement Learning (RL)
techniques so that prices are maximized while keeping a balance between revenue
and fairness. We demonstrate that RL provides two main features to support
fairness in dynamic pricing: on the one hand, RL is able to learn from recent
experience, adapting the pricing policy to complex market environments; on the
other hand, it provides a trade-off between short and long-term objectives,
hence integrating fairness into the model's core. Considering these two
features, we propose the application of RL for revenue optimization, with the
additional integration of fairness as part of the learning procedure by using
Jain's index as a metric. Results in a simulated environment show a significant
improvement in fairness while at the same time maintaining optimisation of
revenue.
Complex-Valued Restricted Boltzmann Machine for Direct Speech Parameterization from Complex Spectra
Authors: Toru Nakashika, Shinji Takaki, Junichi Yamagishi
Date: 2018-03-27 00:00:00
Tasks: Decoder

This paper describes a novel energy-based probabilistic distribution that
represents complex-valued data and explains how to apply it to direct feature
extraction from complex-valued spectra. The proposed model, the complex-valued
restricted Boltzmann machine (CRBM), is designed to deal with complex-valued
visible units as an extension of the well-known restricted Boltzmann machine
(RBM). Like the RBM, the CRBM learns the relationships between visible and
hidden units without having connections between units in the same layer, which
dramatically improves training efficiency by using Gibbs sampling or
contrastive divergence (CD). Another important characteristic is that the CRBM
also has connections between real and imaginary parts of each of the
complex-valued visible units that help represent the data distribution in the
complex domain. In speech signal processing, classification and generation
features are often based on amplitude spectra (e.g., MFCC, cepstra, and
mel-cepstra) even if they are calculated from complex spectra, and they ignore
phase information. In contrast, the proposed feature extractor using the CRBM
directly encodes the complex spectra (or another complex-valued representation
of the complex spectra) into binary-valued latent features (hidden units).
Since the visible-hidden connections are undirected, we can also recover
(decode) the complex spectra from the latent features directly. Our speech
coding experiments demonstrated that the CRBM outperformed other speech coding
methods, such as methods using the conventional RBM, the mel-log spectrum
approximate (MLSA) decoder, etc.
Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification
Authors: Jianlou Si, Honggang Zhang, Chun-Guang Li, Jason Kuen, Xiangfei Kong, Alex C. Kot, Gang Wang
Date: 2018-03-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Person Re-Identification, Triplet

Typical person re-identification (ReID) methods usually describe each
pedestrian with a single feature vector and match them in a task-specific
metric space. However, the methods based on a single feature vector are not
sufficient enough to overcome visual ambiguity, which frequently occurs in real
scenario. In this paper, we propose a novel end-to-end trainable framework,
called Dual ATtention Matching network (DuATM), to learn context-aware feature
sequences and perform attentive sequence comparison simultaneously. The core
component of our DuATM framework is a dual attention mechanism, in which both
intra-sequence and inter-sequence attention strategies are used for feature
refinement and feature-pair alignment, respectively. Thus, detailed visual cues
contained in the intermediate feature sequences can be automatically exploited
and properly compared. We train the proposed DuATM network as a siamese network
via a triplet loss assisted with a de-correlation loss and a cross-entropy
loss. We conduct extensive experiments on both image and video based ReID
benchmark datasets. Experimental results demonstrate the significant advantages
of our approach compared to the state-of-the-art methods.
Image-based deep learning for classification of noise transients in gravitational wave detectors
Authors: Massimiliano Razzano, Elena Cuoco
Date: 2018-03-27 00:00:00
Tasks: Astronomy, Classification, General Classification, Sensitivity

The detection of gravitational waves has inaugurated the era of gravitational
astronomy and opened new avenues for the multimessenger study of cosmic
sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo
interferometers will probe a much larger volume of space and expand the
capability of discovering new gravitational wave emitters. The characterization
of these detectors is a primary task in order to recognize the main sources of
noise and optimize the sensitivity of interferometers. Glitches are transient
noise events that can impact the data quality of the interferometers and their
classification is an important task for detector characterization. Deep
learning techniques are a promising tool for the recognition and classification
of glitches. We present a classification pipeline that exploits convolutional
neural networks to classify glitches starting from their time-frequency
evolution represented as images. We evaluated the classification accuracy on
simulated glitches, showing that the proposed algorithm can automatically
classify glitches on very fast timescales and with high accuracy, thus
providing a promising tool for online detector characterization.
Image Semantic Transformation: Faster, Lighter and Stronger
Authors: Dasong Li, Jianbo Wang
Date: 2018-03-27 00:00:00

We propose Image-Semantic-Transformation-Reconstruction-Circle(ISTRC) model,
a novel and powerful method using facenet's Euclidean latent space to
understand the images. As the name suggests, ISTRC construct the circle, able
to perfectly reconstruct images. One powerful Euclidean latent space embedded
in ISTRC is FaceNet's last layer with the power of distinguishing and
understanding images. Our model will reconstruct the images and manipulate
Euclidean latent vectors to achieve semantic transformations and semantic
images arthimetic calculations. In this paper, we show that ISTRC performs 10
high-level semantic transformations like "Male and female","add smile","open
mouth", "deduct beard or add mustache", "bigger/smaller nose", "make older and
younger", "bigger lips", "bigger eyes", "bigger/smaller mouths" and "more
attractive". It just takes 3 hours(GTX 1080) to train the models of 10 semantic
transformations.
Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions
Authors: Zheng Qin, Zhaoning Zhang, Dongsheng Li, Yiming Zhang, Yuxing Peng
Date: 2018-03-27 00:00:00
Tasks: GPU

Depthwise convolutions provide significant performance benefits owing to the
reduction in both parameters and mult-adds. However, training depthwise
convolution layers with GPUs is slow in current deep learning frameworks
because their implementations cannot fully utilize the GPU capacity. To address
this problem, in this paper we present an efficient method (called diagonalwise
refactorization) for accelerating the training of depthwise convolution layers.
Our key idea is to rearrange the weight vectors of a depthwise convolution into
a large diagonal weight matrix so as to convert the depthwise convolution into
one single standard convolution, which is well supported by the cuDNN library
that is highly-optimized for GPU computations. We have implemented our training
method in five popular deep learning frameworks. Evaluation results show that
our proposed method gains $15.4\times$ training speedup on Darknet, $8.4\times$
on Caffe, $5.4\times$ on PyTorch, $3.5\times$ on MXNet, and $1.4\times$ on
TensorFlow, compared to their original implementations of depthwise
convolutions.
Alive Caricature from 2D to 3D
Authors: Qianyi Wu, Juyong Zhang, Yu-Kun Lai, Jianmin Zheng, Jianfei Cai
Date: 2018-03-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Caricature

Caricature is an art form that expresses subjects in abstract, simple and
exaggerated view. While many caricatures are 2D images, this paper presents an
algorithm for creating expressive 3D caricatures from 2D caricature images with
a minimum of user interaction. The key idea of our approach is to introduce an
intrinsic deformation representation that has a capacity of extrapolation
enabling us to create a deformation space from standard face dataset, which
maintains face constraints and meanwhile is sufficiently large for producing
exaggerated face models. Built upon the proposed deformation representation, an
optimization model is formulated to find the 3D caricature that captures the
style of the 2D caricature image automatically. The experiments show that our
approach has better capability in expressing caricatures than those fitting
approaches directly using classical parametric face models such as 3DMM and
FaceWareHouse. Moreover, our approach is based on standard face datasets and
avoids constructing complicated 3D caricature training set, which provides
great flexibility in real applications.