[r/ML] [D] Thinking about augmentation as invariance assumptions

Summary

This discussion highlights that data augmentation in machine learning is often applied heuristically, based on intuition or borrowed practices, rather than systematic reasoning. The core challenge lies in understanding the specific invariance assumptions each transform imposes, assessing its validity and strength, and recognizing when it might corrupt the training signal. It emphasizes the need for more principled thinking about augmentation strategies to improve model generalization.

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