The using of data augmentation in machine learning in image processing tasks in the face of data scarcity

Abstract

The article presents the results of a study of the efficiency of various neural networks in the limited conditions of the source data and with a number of simple augmentations. In this case, the dependences were obtained for a serial neural network with back propagation of error. For data augmentation, the simplest transformations were used, including the letters tilting (italics), changing the color of letters (from black to red), as well as distortion of the reference images with white Gaussian noise at a signal-to-noise ratio q from 1 to 10. It is shown that the best results of recognition of letters of the Russian alphabet are provided by a network for which all the augmentation methods discussed in this work were used. A study of the dependence of recognition accuracy on the signal-to-noise ratio in all trained neural networkswas also conducted.

Hierarchical Concept Learning by Fuzzy Semantic Cells

“…How to learn concepts with limited data is also a fundamental topic. As the number of samples is limited, prior knowledge [35], strong assumptions [36], appropriate augmentation [37,38] or transferred knowledge [39] is important. They can serve as an important inductive bias for out-of-distribution samples or regularize the model to avoid potential overfitting.…”

Fruit quality and defect image classification with conditional GAN data augmentation

“…Image recognition tasks for Convolutional Neural Network image classification are affected by data scarcity due to their data requirements [26,27], where many generative models have been recommended to alleviate such issues [28,29]. Generative models have also been noted to positively impact biological signal classification [30,31], semantic Imageto-Image Translation [32], speech processing [33,34], and Human Activity Recognition [35,36] among many others.…”

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“…However, the authors also do not consider the problem of determining the coordinates of apples in three-dimensional space. The authors of [25] show that customized training and the use of image augmentation [26] lead to an increase in the quality of such systems. Xuan G. et al [27] achieve f-measure indices up to 91-94% under different illumination conditions on green apples and 94-95% on red apples.…”