Rejection strategy for Convolutional Neural Network by adaptive topology applied to handwritten digits recognition
Identifieur interne : 005F01 ( Main/Merge ); précédent : 005F00; suivant : 005F02Rejection strategy for Convolutional Neural Network by adaptive topology applied to handwritten digits recognition
Auteurs : Hubert Cecotti ; Abdel Belaïd [France]Source :
English descriptors
Abstract
In this paper, we propose a rejection strategy for convolutional neural network models. The purpose of this work is to adapt the network's topology in function of the geometrical error. A self-organizing map is used to change the links between the layers leading to a geometric image transformation occurring directly inside the network. Instead of learning all the possible deformation of a pattern, ambiguous patterns are rejected and the network's topology is modified in function of their geometric errors thanks to a specialized self-organizing map. Our objective is to show how an adaptive topology, without a new learning, can improve the recognition of rejected patterns in the case of handwritten digits.
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<author><name sortKey="Belaid, Abdel" sort="Belaid, Abdel" uniqKey="Belaid A" first="Abdel" last="Belaid">Abdel Belaïd</name>
<affiliation><country>France</country>
<placeName><settlement type="city">Nancy</settlement>
<region type="region" nuts="2">Grand Est</region>
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<orgName type="university">Université de Lorraine</orgName>
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<orgName type="institution">Institut national de recherche en informatique et en automatique</orgName>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>adaptive topology</term>
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<front><div type="abstract" xml:lang="en" wicri:score="3157">In this paper, we propose a rejection strategy for convolutional neural network models. The purpose of this work is to adapt the network's topology in function of the geometrical error. A self-organizing map is used to change the links between the layers leading to a geometric image transformation occurring directly inside the network. Instead of learning all the possible deformation of a pattern, ambiguous patterns are rejected and the network's topology is modified in function of their geometric errors thanks to a specialized self-organizing map. Our objective is to show how an adaptive topology, without a new learning, can improve the recognition of rejected patterns in the case of handwritten digits.</div>
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