Learning Instance-Specific Augmentations by Capturing Local Invariances
This addresses the limitation of previous augmentation methods that assume independence between inputs and transformations, offering a more flexible approach for machine learning practitioners.
The paper tackles the problem of learning input-specific data augmentations by introducing InstaAug, a method that captures local invariances through a learnable invariance module, resulting in improved performance on supervised and self-supervised tasks.
We introduce InstaAug, a method for automatically learning input-specific augmentations from data. Previous methods for learning augmentations have typically assumed independence between the original input and the transformation applied to that input. This can be highly restrictive, as the invariances we hope our augmentation will capture are themselves often highly input dependent. InstaAug instead introduces a learnable invariance module that maps from inputs to tailored transformation parameters, allowing local invariances to be captured. This 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.