Abstract

We studied the possibility of extracting object hierarchy information from a trained neural network by analyzing the errors obtained on a test sample using an approach based on singular value decomposition of the confusion matrix. Experiments indicate that the methods investigated in this paper can be used to obtain a tentative clustering of classes. In addition, we show that the number of connections within a fully connected layer of a convolutional neural network can be reduced without adversely affecting recognition accuracy for a test sample using locally connected layers. At the same time, however, our experiments did not show that a layer organization consistent with the object hierarchy led to any improvement of the results.

© 2017 Optical Society of America

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