A New Rejection Strategy for Convolutional Neural Network by Adaptive Topology

Abstract

We presented a model combining supervised learning for the classification stage and a specific not-supervised model for the test stage. It allows changing the topology of the supervised part for improving the reject quality. Within the framework of the recognition of disturbed forms, our strategy was based on the adaptability of the classifier instead of, for example, making it learn more samples by artificially creating all the possible deformations, our approach consisted in defining the type of problematical transformation, by changing the localities observed by the principal classifier. The system makes it possible indeed to refine the results till it makes possible to raise ambiguities for certain confusions but there remains still depend on the effectiveness of the initial rejection and the assumptions concerning the choice of the model of class. On the level of the prospects, if the type of deformation is perfectly known: cut, rotation, problem of shift, it would be possible to introduce new properties inside the self-organizing map to speed up its convergence.