  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.
