Advanced Random Mix Augmentation : Data Augmentation Method for Improving Performance of Image Classification in Deep Learning Using Unduplicated Image Processing Combinations

Abstract

Data augmentation is a commonly used method for improving deep learning models in image classification. By adding slightly modified images that do not change the label of the original image to the training data set, the trained model becomes more robust against diverse characteristics of the input image. In this study, we propose a new data augmentation method by improving a previously-known random augmentation method. Our method consists of three steps; 1) determine the set of image modification operators and the number of augmented images, 2) determine the sequence of the image modification operators so that no duplicated sequences are generated, and 3) apply the sequence to augment images. The variety of augmentation is further increased by randomly determining the level (intensity) and the weight of combining the sequences. We applied our method on the CIFAR dataset and show that our method outperforms existing methods.