The Effectiveness of Data Augmentation in Image Classification using Deep Learning

TLDR

Data augmentation using simple transformations such as cropping, rotating, and flipping has been shown to improve image classification performance. The study investigates and compares multiple data augmentation strategies and introduces a neural augmentation approach that learns the most effective augmentations for a classifier. By limiting training to a small subset of ImageNet, the authors evaluate standard augmentations, experiment with GAN‑generated images, and develop a neural augmentation method that selects optimal transformations. The neural augmentation technique demonstrates both successes and limitations across several datasets.

Abstract

In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification. Previous work has demonstrated the effectiveness of data augmentation through simple techniques, such as cropping, rotating, and flipping input images. We artificially constrain our access to data to a small subset of the ImageNet dataset, and compare each data augmentation technique in turn. One of the more successful data augmentations strategies is the traditional transformations mentioned above. We also experiment with GANs to generate images of different styles. Finally, we propose a method to allow a neural net to learn augmentations that best improve the classifier, which we call neural augmentation. We discuss the successes and shortcomings of this method on various datasets.