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.

翻译：我们提出 InstaAug 方法，用于从数据中自动学习针对特定输入的扩充策略。先前的扩充学习方法通常假设原始输入与其所施加的变换相互独立。这种假设具有高度局限性，因为我们期望通过扩充捕捉的不变性往往本身强烈依赖于输入。InstaAug 则引入了一个可学习的不变性模块，将输入映射到定制的变换参数上，从而能够捕获局部不变性。该模块可与下游模型以完全端到端的方式联合训练，也可针对预训练模型进行独立学习。实验表明，InstaAug 能在多种变换类别中学到有意义的依赖于输入的扩充策略，进而为监督和自监督任务带来更优的性能表现。