NEGATIVE DATA AUGMENTATION

Abstract

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.

1. INTRODUCTION

Data augmentation strategies for synthesizing new data in a way that is consistent with an underlying task are extremely effective in both supervised and unsupervised learning (Oord et al., 2018; Zhang et al., 2016; Noroozi & Favaro, 2016; Asano et al., 2019) . Because they operate at the level of samples, they can be combined with most learning algorithms. They allow for the incorporation of prior knowledge (inductive bias) about properties of typical samples from the underlying data distribution (Jaiswal et al., 2018; Antoniou et al., 2017) , e.g., by leveraging invariances to produce additional "positive" examples of how a task should be solved. To enable users to specify an even wider range of inductive biases, we propose to leverage an alternative and complementary source of prior knowledge that specifies how a task should not be solved. We formalize this intuition by assuming access to a way of generating samples that are guaranteed to be out-of-support for the data distribution, which we call a Negative Data Augmentation (NDA). Intuitively, negative out-of-distribution (OOD) samples can be leveraged as a useful inductive bias because they provide information about the support of the data distribution to be learned by the model. For example, in a density estimation problem we can bias the model to avoid putting any probability mass in regions which we know a-priori should have zero probability. This can be an effective prior if the negative samples cover a sufficiently large area. The best NDA candidates are ones that expose common pitfalls of existing models, such as prioritizing local structure over global

