  Data augmentation is often used to enlarge datasets with synthetic samples
generated in accordance with the underlying data distribution. To enable a
wider range of augmentations, we explore negative data augmentation strategies
(NDA)that intentionally create out-of-distribution samples. We show that such
negative out-of-distribution samples provide information on the support of the
data distribution, and can be leveraged for generative modeling and
representation learning. We introduce a new GAN training objective where we use
NDA as an additional source of synthetic data for the discriminator. We prove
that under suitable conditions, optimizing the resulting objective still
recovers the true data distribution but can directly bias the generator towards
avoiding samples that lack the desired structure. Empirically, models trained
with our method achieve improved conditional/unconditional image generation
along with improved anomaly detection capabilities. Further, we incorporate the
same negative data augmentation strategy in a contrastive learning framework
for self-supervised representation learning on images and videos, achieving
improved performance on downstream image classification, object detection, and
action recognition tasks. These results suggest that prior knowledge on what
does not constitute valid data is an effective form of weak supervision across
a range of unsupervised learning tasks.
