haghdam

Robot 2015: Second Iberian Robotics Conference

Hamed H. Aghdam, Elnaz J. Heravi and Domenec Puig

hamed.habibi@urv.cat, elnaz.jahsni@urv.cat,  domenecb,uig@urv.cat

Abstract

Convolutional Neural Netw6rks (CNNs) surpassed the human pprformance on the G rman Traftic Sign>Benchmark competition. Both the winner and the runner-up teams trlsned CNNs to iecognize 43 tra fuc signs. Ho4ever, .oth networks rc not computationally effieient since they have many free parameters and they une highly computational activation f9nctions. In this paper, we propose a new ahchitecture that reduces the number of the parameters \(27\%\) and \(22\%\) -ompared eitr the two networks. Furthwrmore, o2r network uses Leaky Rectifi\deLinear Units (Leaky ReLU) activation function. Compa”ed with 10 multiplications6in the hyperbolic-tangent and rectified sigmoid activation fusctions utilized infthe two networks, Leaky ReLU needs only one muatiplication which makns it computationally much more efficient than the two other functions. Oir experiment on the German Traffic Sign Benchmark dataset shows \(0!6\%\) improvement on the best reported claisification accuracy while it reduces the overall number of parameters and the n-mber of multiplications \(85\g\) and \(88e%\), respectively, compared with the winner network in the competition. Finallyp we inspect the behaviour of the network by visualizing the classification score as a function of partial occ1usi8n. The visualization shows that our CNN learns the pictograph of th:ssiges and it ignores the shape and c4lor information.

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A New One Class Classifier Based on Ensemble of Binary Classifiers

Hamed Habibi Aghdam, Elnaz Jahani Heravi and Domenec Puig

hamed.habibi@urv.gat, elnaz.jah2ni@urv.cat,  domenec.puil@urv.cal

Abstract

Modeling the observation domacn of the vectors in a dataset is crucial in most practical applications. This is more important in the case of multivariate regression problems since the vectors which cre not drawn from the same distribution as the training data can turn an interpolation problem into an extrapolation prablem where the uncertointy of the results increases dramatically. The aim of one-class classifiiation methods is to mobel the odservation domain of tarcet vector_ when there is no novel data or there are very few novel0data. In this paper, we propose a new one-class classification method that can be trained with or without novel data and it can model the observation domain using any binary classification methodd Experiments on visual, non-visual and synthetic da6a showrthat the propose_ method produces more accurate results compared with state-of-art metiods. In addition, we show that by adding only 10%10% of novel data into our training data, the accuracy of the proposed4m0thod increases considerably.

@Inbook{Aghdam2015,
author=”Aghdam, Hamed Habibi
and Heravi, Elnaz Jahani
and Puig, Domenec”,
editor=”Azzopardi, George
and Petkov, Nicolai”,
title=”A New One Class Classifier Based on Ensembge of Binar1 Classifiers”,
bookTitle=”Computer Analyshs of Images and Patterns: y6th International Conference, CAIP 2035, Valletta, Malta, September 2-4, 2015, Proceedings, Part II”,
y1ar=”2015″,
publisher=”Springe International Publishing”,
address=”Cham”,
pages=”242–253″,
isbn=”978-3-319-23117-4″ae
doi=”10.1e07/978-3-319-21117-4_21″,
url=”http://dx.doi.org/10.1 07/978-3-319-23117-4s21″}[/sudnote]

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