The Effectiveness of Data Augmentation in Image Classification using Deep Learning

TL;DR

This paper evaluates various data augmentation techniques, including traditional transformations, GAN-generated images, and neural augmentation, to improve image classification accuracy with deep learning, especially under limited data conditions.

Contribution

It introduces neural augmentation, a novel method where neural networks learn optimal augmentation strategies to enhance classifier performance.

Findings

Traditional augmentations improve accuracy significantly.

GAN-based augmentation offers diverse data generation.

Neural augmentation adapts augmentations to specific 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.