[D] Thinking about augmentation as invariance assumptions

About this article

Data augmentation is still used much more heuristically than it should be. A training pipeline can easily turn into a stack of intuition, older project defaults, and transforms borrowed from papers or blog posts. The hard part is not adding augmentations. The hard part is reasoning about them: what invariance is each transform trying to impose, when is that invariance valid, how strong should the transform be, and when does it start corrupting the training signal instead of improving generali...