Деформирующие преобразования изображений и их применение при аугментации данных для обучения глубоких нейронных сетей

Abstract

The paper focuses on the improvement of the quality of learning for deep neural networks for a small data set in a classification task. One of the possible approaches to improve the quality of learning is researched which is based on the use of data augmentation (artificial reproduction of the data set) by image warping. The presented mathematical model and fast algorithm for warping make it possible to transform the original image while preserving its structural basis. The proposed algorithm is used to augment image data sets containing a small number of training samples. The augmentation consists of two stages including horizontal mirroring and warping of each of the samples. The effectiveness of such augmentation is tested through the training of neural networks of various types: convolutional neural networks (CNN) of a standard architecture and deep residual networks (DRN). A specific feature of the implemented approach for the solution of the problem under consideration consists in the refusal to use pre-trained neural networks with a large number of layers as well as further transfer learning, since their application incurs costs in terms of the computational resources. The paper shows that the efficiency of image classification when implementing the proposed method of augmenting training data on small and medium-sized data sets increases to statistically significant values of the metric used.