The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Published in arXiv.org (2017)
TL
TL;DR
A method to allow a neural net to learn augmentations that best improve the classifier, which is called neural augmentation is proposed, and the successes and shortcomings of this method are discussed.
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.
References18 items
1
DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data
2
Megapixel Size Image Creation using Generative Adversarial Networks
3
Convergence Analysis of Batch Normalization for Deep Neural Nets
4
On the Effects of Batch and Weight Normalization in Generative Adversarial Networks
5