Use of Images Augmentation and Implementation of Doubly Stochastic Models for Improving Accuracy of Recognition Algorithms Based on Convolutional Neural Networks

Abstract

The article proposes methods for increasing the efficiency of pattern recognition in images using convolutional neural networks in conditions of insufficient reference images. It is proposed to use various kinds of image transformations to generate new images and increase the training sample volume. In particular, methods of scaling, adding noise, blurring, etc. are proposed. In addition to standard image transformations, it is supposed to increase the database by presenting patterns using a doubly stochastic model. In addition, the need for the use of regularization in training is noted. The recognition results are compared for two convolutional neural networks that differ only in the number of neurons for different initial data. It is shown that the highest recognition accuracy is provided by a network trained on a large number of augmentations in combination with images generated by a doubly stochastic model.