pwc-archive/papers-with-abstracts huggingface dataset:
Fingerprint Spoof Buster
Authors: Tarang Chugh, Kai Cao, Anil K. Jain
Date: 2017-12-12 00:00:00

The primary purpose of a fingerprint recognition system is to ensure a
reliable and accurate user authentication, but the security of the recognition
system itself can be jeopardized by spoof attacks. This study addresses the
problem of developing accurate, generalizable, and efficient algorithms for
detecting fingerprint spoof attacks. Specifically, we propose a deep
convolutional neural network based approach utilizing local patches centered
and aligned using fingerprint minutiae. Experimental results on three
public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed
approach provides state-of-the-art accuracies in fingerprint spoof detection
for intra-sensor, cross-material, cross-sensor, as well as cross-dataset
testing scenarios. For example, in LivDet 2015, the proposed approach achieves
99.03% average accuracy over all sensors compared to 95.51% achieved by the
LivDet 2015 competition winners. Additionally, two new fingerprint presentation
attack datasets containing more than 20,000 images, using two different
fingerprint readers, and over 12 different spoof fabrication materials are
collected. We also present a graphical user interface, called Fingerprint Spoof
Buster, that allows the operator to visually examine the local regions of the
fingerprint highlighted as live or spoof, instead of relying on only a single
score as output by the traditional approaches.
Image Registration for the Alignment of Digitized Historical Documents
Authors: AmirAbbas Davari, Tobias Lindenberger, Armin Häberle, Vincent Christlein, Andreas Maier, Christian Riess
Date: 2017-12-12 00:00:00
Tasks: Image Registration

In this work, we conducted a survey on different registration algorithms and
investigated their suitability for hyperspectral historical image registration
applications. After the evaluation of different algorithms, we choose an
intensity based registration algorithm with a curved transformation model. For
the transformation model, we select cubic B-splines since they should be
capable to cope with all non-rigid deformations in our hyperspectral images.
From a number of similarity measures, we found that residual complexity and
localized mutual information are well suited for the task at hand. In our
evaluation, both measures show an acceptable performance in handling all
difficulties, e.g., capture range, non-stationary and spatially varying
intensity distortions or multi-modality that occur in our application.
Learning a Complete Image Indexing Pipeline
Authors: Himalaya Jain, Joaquin Zepeda, Patrick Pérez, Rémi Gribonval
Date: 2017-12-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Clustering

To work at scale, a complete image indexing system comprises two components:
An inverted file index to restrict the actual search to only a subset that
should contain most of the items relevant to the query; An approximate distance
computation mechanism to rapidly scan these lists. While supervised deep
learning has recently enabled improvements to the latter, the former continues
to be based on unsupervised clustering in the literature. In this work, we
propose a first system that learns both components within a unifying neural
framework of structured binary encoding.
Enhancing approximation abilities of neural networks by training derivatives
Authors: V. I. Avrutskiy
Date: 2017-12-12 00:00:00
Tasks: GPU

A method to increase the precision of feedforward networks is proposed. It requires a prior knowledge of a target function derivatives of several orders and uses this information in gradient based training. Forward pass calculates not only the values of the output layer of a network but also their derivatives. The deviations of those derivatives from the target ones are used in an extended cost function and then backward pass calculates the gradient of the extended cost with respect to weights, which can then be used by any weights update algorithm. Despite a substantial increase in arithmetic operations per pattern (if compared to the conventional training), the extended cost allows to obtain 140--1000 times more accurate approximation for simple cases if the total number of operations is equal. This precision also happens to be out of reach for the regular cost function. The method fits well into the procedure of solving differential equations with neural networks. Unlike training a network to match some target mapping, which requires an explicit use of the target derivatives in the extended cost function, the cost function for solving a differential equation is based on the deviation of the equation's residual from zero and thus can be extended by differentiating the equation itself, which does not require any prior knowledge. Solving an equation with such a cost resulted in 13 times more accurate result and could be done with 3 times larger grid step. GPU-efficient algorithm for calculating the gradient of the extended cost function is proposed.
Data Distillation: Towards Omni-Supervised Learning
Authors: Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, Kaiming He
Date: 2017-12-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Keypoint Detection, object-detection, Object Detection

We investigate omni-supervised learning, a special regime of semi-supervised
learning in which the learner exploits all available labeled data plus
internet-scale sources of unlabeled data. Omni-supervised learning is
lower-bounded by performance on existing labeled datasets, offering the
potential to surpass state-of-the-art fully supervised methods. To exploit the
omni-supervised setting, we propose data distillation, a method that ensembles
predictions from multiple transformations of unlabeled data, using a single
model, to automatically generate new training annotations. We argue that visual
recognition models have recently become accurate enough that it is now possible
to apply classic ideas about self-training to challenging real-world data. Our
experimental results show that in the cases of human keypoint detection and
general object detection, state-of-the-art models trained with data
distillation surpass the performance of using labeled data from the COCO
dataset alone.
3D Object Classification via Spherical Projections
Authors: Zhangjie Cao, Qi-Xing Huang, Karthik Ramani
Date: 2017-12-12 00:00:00
Tasks: 3D Classification, 3D Object Classification, Classification, General Classification, Object

In this paper, we introduce a new method for classifying 3D objects. Our main
idea is to project a 3D object onto a spherical domain centered around its
barycenter and develop neural network to classify the spherical projection. We
introduce two complementary projections. The first captures depth variations of
a 3D object, and the second captures contour-information viewed from different
angles. Spherical projections combine key advantages of two main-stream 3D
classification methods: image-based and 3D-based. Specifically, spherical
projections are locally planar, allowing us to use massive image datasets (e.g,
ImageNet) for pre-training. Also spherical projections are similar to
voxel-based methods, as they encode complete information of a 3D object in a
single neural network capturing dependencies across different views. Our novel
network design can fully utilize these advantages. Experimental results on
ModelNet40 and ShapeNetCore show that our method is superior to prior methods.
Oversampling for Imbalanced Learning Based on K-Means and SMOTE
Authors: Felix Last, Georgios Douzas, Fernando Bacao
Date: 2017-11-02 00:00:00
Tasks: Classification, Clustering, Data-to-Text Generation, General Classification

Learning from class-imbalanced data continues to be a common and challenging
problem in supervised learning as standard classification algorithms are
designed to handle balanced class distributions. While different strategies
exist to tackle this problem, methods which generate artificial data to achieve
a balanced class distribution are more versatile than modifications to the
classification algorithm. Such techniques, called oversamplers, modify the
training data, allowing any classifier to be used with class-imbalanced
datasets. Many algorithms have been proposed for this task, but most are
complex and tend to generate unnecessary noise. This work presents a simple and
effective oversampling method based on k-means clustering and SMOTE
oversampling, which avoids the generation of noise and effectively overcomes
imbalances between and within classes. Empirical results of extensive
experiments with 71 datasets show that training data oversampled with the
proposed method improves classification results. Moreover, k-means SMOTE
consistently outperforms other popular oversampling methods. An implementation
is made available in the python programming language.
Deception Detection in Videos
Authors: Zhe Wu, Bharat Singh, Larry S. Davis, V. S. Subrahmanian
Date: 2017-12-12 00:00:00
Tasks: Action Recognition, Deception Detection, Deception Detection In Videos, Temporal Action Localization

We present a system for covert automated deception detection in real-life
courtroom trial videos. We study the importance of different modalities like
vision, audio and text for this task. On the vision side, our system uses
classifiers trained on low level video features which predict human
micro-expressions. We show that predictions of high-level micro-expressions can
be used as features for deception prediction. Surprisingly, IDT (Improved Dense
Trajectory) features which have been widely used for action recognition, are
also very good at predicting deception in videos. We fuse the score of
classifiers trained on IDT features and high-level micro-expressions to improve
performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio
domain also provide a significant boost in performance, while information from
transcripts is not very beneficial for our system. Using various classifiers,
our automated system obtains an AUC of 0.877 (10-fold cross-validation) when
evaluated on subjects which were not part of the training set. Even though
state-of-the-art methods use human annotations of micro-expressions for
deception detection, our fully automated approach outperforms them by 5%. When
combined with human annotations of micro-expressions, our AUC improves to
0.922. We also present results of a user-study to analyze how well do average
humans perform on this task, what modalities they use for deception detection
and how they perform if only one modality is accessible. Our project page can
be found at \url{https://doubaibai.github.io/DARE/}.
Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks
Authors: Alexander Sage, Eirikur Agustsson, Radu Timofte, Luc van Gool
Date: 2017-12-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Clustering, Diversity

Designing a logo for a new brand is a lengthy and tedious back-and-forth
process between a designer and a client. In this paper we explore to what
extent machine learning can solve the creative task of the designer. For this,
we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web.
Training Generative Adversarial Networks (GANs) for logo synthesis on such
multi-modal data is not straightforward and results in mode collapse for some
state-of-the-art methods. We propose the use of synthetic labels obtained
through clustering to disentangle and stabilize GAN training. We are able to
generate a high diversity of plausible logos and we demonstrate latent space
exploration techniques to ease the logo design task in an interactive manner.
Moreover, we validate the proposed clustered GAN training on CIFAR 10,
achieving state-of-the-art Inception scores when using synthetic labels
obtained via clustering the features of an ImageNet classifier. GANs can cope
with multi-modal data by means of synthetic labels achieved through clustering,
and our results show the creative potential of such techniques for logo
synthesis and manipulation. Our dataset and models will be made publicly
available at https://data.vision.ee.ethz.ch/cvl/lld/.
Android Malware Characterization using Metadata and Machine Learning Techniques
Authors: Ignacio Martín, José Alberto Hernández, Alfonso Muñoz, Antonio Guzmán
Date: 2017-12-12 00:00:00
Tasks: BIG-bench Machine Learning

Android Malware has emerged as a consequence of the increasing popularity of
smartphones and tablets. While most previous work focuses on inherent
characteristics of Android apps to detect malware, this study analyses indirect
features and meta-data to identify patterns in malware applications. Our
experiments show that: (1) the permissions used by an application offer only
moderate performance results; (2) other features publicly available at Android
Markets are more relevant in detecting malware, such as the application
developer and certificate issuer, and (3) compact and efficient classifiers can
be constructed for the early detection of malware applications prior to code
inspection or sandboxing.
Forward and Reverse Gradient-Based Hyperparameter Optimization
Authors: Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
Date: 2017-03-06 00:00:00
Proceeding: ICML 2017 8
Tasks: Hyperparameter Optimization

We study two procedures (reverse-mode and forward-mode) for computing the
gradient of the validation error with respect to the hyperparameters of any
iterative learning algorithm such as stochastic gradient descent. These
procedures mirror two methods of computing gradients for recurrent neural
networks and have different trade-offs in terms of running time and space
requirements. Our formulation of the reverse-mode procedure is linked to
previous work by Maclaurin et al. [2015] but does not require reversible
dynamics. The forward-mode procedure is suitable for real-time hyperparameter
updates, which may significantly speed up hyperparameter optimization on large
datasets. We present experiments on data cleaning and on learning task
interactions. We also present one large-scale experiment where the use of
previous gradient-based methods would be prohibitive.
Hawkes Processes for Invasive Species Modeling and Management
Authors: Amrita Gupta, Mehrdad Farajtabar, Bistra Dilkina, Hongyuan Zha
Date: 2017-12-12 00:00:00
Tasks: Management

The spread of invasive species to new areas threatens the stability of
ecosystems and causes major economic losses in agriculture and forestry. We
propose a novel approach to minimizing the spread of an invasive species given
a limited intervention budget. We first model invasive species propagation
using Hawkes processes, and then derive closed-form expressions for
characterizing the effect of an intervention action on the invasion process. We
use this to obtain an optimal intervention plan based on an integer programming
formulation, and compare the optimal plan against several
ecologically-motivated heuristic strategies used in practice. We present an
empirical study of two variants of the invasive control problem: minimizing the
final rate of invasions, and minimizing the number of invasions at the end of a
given time horizon. Our results show that the optimized intervention achieves
nearly the same level of control that would be attained by completely
eradicating the species, with a 20% cost saving. Additionally, we design a
heuristic intervention strategy based on a combination of the density and life
stage of the invasive individuals, and find that it comes surprisingly close to
the optimized strategy, suggesting that this could serve as a good rule of
thumb in invasive species management.
CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
Authors: Farshid Rayhan, Sajid Ahmed, Asif Mahbub, Md. Rafsan Jani, Swakkhar Shatabda, Dewan Md. Farid
Date: 2017-12-12 00:00:00
Tasks: Clustering, Ensemble Learning, General Classification, imbalanced classification

Class imbalance classification is a challenging research problem in data
mining and machine learning, as most of the real-life datasets are often
imbalanced in nature. Existing learning algorithms maximise the classification
accuracy by correctly classifying the majority class, but misclassify the
minority class. However, the minority class instances are representing the
concept with greater interest than the majority class instances in real-life
applications. Recently, several techniques based on sampling methods
(under-sampling of the majority class and over-sampling the minority class),
cost-sensitive learning methods, and ensemble learning have been used in the
literature for classifying imbalanced datasets. In this paper, we introduce a
new clustering-based under-sampling approach with boosting (AdaBoost)
algorithm, called CUSBoost, for effective imbalanced classification. The
proposed algorithm provides an alternative to RUSBoost (random under-sampling
with AdaBoost) and SMOTEBoost (synthetic minority over-sampling with AdaBoost)
algorithms. We evaluated the performance of CUSBoost algorithm with the
state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost,
SMOTEBoost on 13 imbalance binary and multi-class datasets with various
imbalance ratios. The experimental results show that the CUSBoost is a
promising and effective approach for dealing with highly imbalanced datasets.
ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection
Authors: Sima Behpour, Kris M. Kitani, Brian D. Ziebart
Date: 2017-10-21 00:00:00
Tasks: Data Augmentation, Object, object-detection, Object Detection, Translation

The use of random perturbations of ground truth data, such as random
translation or scaling of bounding boxes, is a common heuristic used for data
augmentation that has been shown to prevent overfitting and improve
generalization. Since the design of data augmentation is largely guided by
reported best practices, it is difficult to understand if those design choices
are optimal. To provide a more principled perspective, we develop a
game-theoretic interpretation of data augmentation in the context of object
detection. We aim to find an optimal adversarial perturbations of the ground
truth data (i.e., the worst case perturbations) that forces the object bounding
box predictor to learn from the hardest distribution of perturbed examples for
better test-time performance. We establish that the game theoretic solution,
the Nash equilibrium, provides both an optimal predictor and optimal data
augmentation distribution. We show that our adversarial method of training a
predictor can significantly improve test time performance for the task of
object detection. On the ImageNet object detection task, our adversarial
approach improves performance by over 16\% compared to the best performing data
augmentation method
Deep Echo State Network (DeepESN): A Brief Survey
Authors: Claudio Gallicchio, Alessio Micheli
Date: 2017-12-12 00:00:00
Tasks: Survey

The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced Deep Echo State Network (DeepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of DeepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions of recurrent layers, i.e. on the bias of depth in RNNs architectural design. In this paper, we summarize the advancements in the development, analysis and applications of DeepESNs.
Generating and Estimating Nonverbal Alphabets for Situated and Multimodal Communications
Authors: Serhii Hamotskyi, Sergii Stirenko, Yuri Gordienko, Anis Rojbi
Date: 2017-12-12 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we discuss the formalized approach for generating and
estimating symbols (and alphabets), which can be communicated by the wide range
of non-verbal means based on specific user requirements (medium, priorities,
type of information that needs to be conveyed). The short characterization of
basic terms and parameters of such symbols (and alphabets) with approaches to
generate them are given. Then the framework, experimental setup, and some
machine learning methods to estimate usefulness and effectiveness of the
nonverbal alphabets and systems are presented. The previous results demonstrate
that usage of multimodal data sources (like wearable accelerometer, heart
monitor, muscle movements sensors, braincomputer interface) along with machine
learning approaches can provide the deeper understanding of the usefulness and
effectiveness of such alphabets and systems for nonverbal and situated
communication. The symbols (and alphabets) generated and estimated by such
methods may be useful in various applications: from synthetic languages and
constructed scripts to multimodal nonverbal and situated interaction between
people and artificial intelligence systems through Human-Computer Interfaces,
such as mouse gestures, touchpads, body gestures, eyetracking cameras,
wearables, and brain-computing interfaces, especially in applications for
elderly care and people with disabilities.
The Zero Resource Speech Challenge 2017
Authors: Ewan Dunbar, Xuan Nga Cao, Juan Benjumea, Julien Karadayi, Mathieu Bernard, Laurent Besacier, Xavier Anguera, Emmanuel Dupoux
Date: 2017-12-12 00:00:00

We describe a new challenge aimed at discovering subword and word units from
raw speech. This challenge is the followup to the Zero Resource Speech
Challenge 2015. It aims at constructing systems that generalize across
languages and adapt to new speakers. The design features and evaluation metrics
of the challenge are presented and the results of seventeen models are
discussed.
Is the Bellman residual a bad proxy?
Authors: Matthieu Geist, Bilal Piot, Olivier Pietquin
Date: 2016-06-24 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper aims at theoretically and empirically comparing two standard
optimization criteria for Reinforcement Learning: i) maximization of the mean
value and ii) minimization of the Bellman residual. For that purpose, we place
ourselves in the framework of policy search algorithms, that are usually
designed to maximize the mean value, and derive a method that minimizes the
residual $\|T_* v_\pi - v_\pi\|_{1,\nu}$ over policies. A theoretical analysis
shows how good this proxy is to policy optimization, and notably that it is
better than its value-based counterpart. We also propose experiments on
randomly generated generic Markov decision processes, specifically designed for
studying the influence of the involved concentrability coefficient. They show
that the Bellman residual is generally a bad proxy to policy optimization and
that directly maximizing the mean value is much better, despite the current
lack of deep theoretical analysis. This might seem obvious, as directly
addressing the problem of interest is usually better, but given the prevalence
of (projected) Bellman residual minimization in value-based reinforcement
learning, we believe that this question is worth to be considered.
Proceedings of NIPS 2017 Workshop on Machine Learning for the Developing World
Authors: Maria De-Arteaga, William Herlands
Date: 2017-11-27 00:00:00
Tasks: BIG-bench Machine Learning

This is the Proceedings of NIPS 2017 Workshop on Machine Learning for the
Developing World, held in Long Beach, California, USA on December 8, 2017
Multi-Speaker Localization Using Convolutional Neural Network Trained with Noise
Authors: Soumitro Chakrabarty, Emanuël. A. P. Habets
Date: 2017-12-12 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

The problem of multi-speaker localization is formulated as a multi-class
multi-label classification problem, which is solved using a convolutional
neural network (CNN) based source localization method. Utilizing the common
assumption of disjoint speaker activities, we propose a novel method to train
the CNN using synthesized noise signals. The proposed localization method is
evaluated for two speakers and compared to a well-known steered response power
method.