Improving the Leaking of Augmentations in Data-Efficient GANs via Adaptive Negative Data Augmentation

Abstract

Data augmentation (DA) has shown its effectiveness in training Data-Efficient GANs (DE-GANs). However, applying DA in DE-GANs results in transforming the distributions of generated data and real data to augmented distributions of generated data and real data. This augmentation process could produce some out-of-distribution samples, known as the leaking of augmentations problem, which is highly undesirable in DE-GANs training. Although some methods propose "leaking-free" DAs for DE-GANs, we theoretically and practically argue that the leaking of augmentations problem still exists in these methods. To alleviate the leaking of augmentations in DE-GANs, in this paper, we propose a simple yet effective method called adaptive negative data augmentation (ANDA) for DE-GANs, with a negligible computational cost increase. Specifically, ANDA adaptively augments the augmented distribution of generated data using the augmented distribution of negative real data, where the negative real data is produced by applying negative data augmentation (NDA) on the real data. In this case, potential leaking samples can be presented as "fake" instances to the discriminator adaptively, which avoids the generator (G) learning such samples, thus resulting in better performance. Extensive experiments on several datasets with different DE-GANs demonstrate that ANDA can effectively alleviate the leaking of augmentations problem during training and achieve better performance. Codes are available at https://github.com/zzhang05/ANDA