Instance-Specific Augmentation: Capturing Local Invariances

Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim

Keywords: inductive bias, data augmentation, invariance

Abstract: We introduce InstaAug, a method for automatically learning input-specific augmentations from data. Previous data augmentation methods have generally assumed independence between the original input and the transformation applied to that input. This can be highly restrictive, as the invariances that the augmentations are based on are themselves often highly input dependent; e.g., we can change a leaf from green to yellow while maintaining its label, but not a lime. InstaAug instead allows for input dependency by introducing an invariance module that maps inputs to tailored transformation distributions. It can be simultaneously trained alongside the downstream model in a fully end-to-end manner, or separately learned for a pre-trained model. We empirically demonstrate that InstaAug learns meaningful input-dependent augmentations for a wide range of transformation classes, which in turn provides better performance on both supervised and self-supervised tasks.

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