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Rejection strategy for convolutional neural network by adaptive topology applied to handwritten digits recognition

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2 Author(s)
H. Cecotti ; LORIA/CNRS, Vandoeuvre-les-Nancy, France ; A. Belaid

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 injunction 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.

Published in:

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

Date of Conference:

29 Aug.-1 Sept. 2005