Automatic Data Augmentation Method with Improved Interpretability for Image Classification in Computer Vision Applications

This paper presents an interpretable automatic data augmentation method. While the mechanism of the existing automatic augmentation methods is not easily understandable, the proposed method seeks to make the process of finding an augmentation policy more clear while maintaining simplicity. The proposed method has been tested on the Fashion-MNIST with a simple convnet and on the CIFAR10 dataset with ResNet50 network. Moreover, it was compared with the RandAugment, Augmix and TrivialAugment methods using benchmark datasets Fashion MNIST-C and CIFAR-10C. The developed method ex-hibits comparable results to that of existing approaches with the key advantage of interpretability.