Research

A fundamental problem in machine learning is to learn representations that are invariant to certain transformations. For example, image representations are desired to be invariant to translation, rotation, changes in color, or background; natural language representations ought to be invariant to named entities. Naturally, data augmentations are a simple yet powerful way to address such invariance. However, such data augmentations requiring either additional data collection or careful engineering to capture all invariances. In this paper, we argue that a simple yet effective additional loss, called Data Augmented Invariant Regularization (DAIR), could improve the performance even further. DAIR promotes additional invariance on top of data augmentations at little marginal cost, and is consistent with any learning model. We empirically evaluate the performance of DAIR on two vision tasks, Colored MNIST and Rotated MNIST, and demonstrate that it provides non-trivial gains beyond data augmentation, outperforming invariant risk minimization.