Rejection strategy for convolutional neural network by
adaptive topology applied to handwritten digits
recognition
Document Analysis and Recognition, 2005. Proceedings.
Eighth International Conference on, 2, page 765--769. (August 2005)DOI: 10.1109/ICDAR.2005.200
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 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.