A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments. (arXiv:2202.08325v1 [cs.LG])

Data-Augmentation (DA) is known to improve performance across tasks and
datasets. We propose a method to theoretically analyze the effect of DA and
study questions such as: how many augmented samples are needed to correctly
estimate the information encoded by that DA? How does the augmentation policy
impact the final parameters of a model? We derive several quantities in
close-form, such as the expectation and variance of an image, loss, and model's
output under a given DA distribution. Those derivations …

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