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Rejection strategy for Convolutional Neural Network by adaptive topology applied to handwritten digits recognition

Hubert Cecotti 1 Abdel Belaïd 1
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LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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.
Keywords : convolutional neural network character recognition adaptive topology rejection strategy
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Conference papers
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https://hal.inria.fr/inria-00000362
Contributor : Hubert Cecotti <>
Submitted on : Tuesday, September 27, 2005 - 3:40:57 PM
Last modification on : Friday, February 26, 2021 - 3:28:06 PM

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  • HAL Id : inria-00000362, version 1

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INRIA | CNRS | LORIA | UNIV-LORRAINE

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Hubert Cecotti, Abdel Belaïd. Rejection strategy for Convolutional Neural Network by adaptive topology applied to handwritten digits recognition. 8th International Conference in Document Analysis and Recognition - ICDAR'05, Aug 2005, Seoul, Korea, pp.765-769. ⟨inria-00000362⟩

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