Regularising for invariance to data augmentation improves supervised learning

@article{Botev2022RegularisingFI,
  title={Regularising for invariance to data augmentation improves supervised learning},
  author={Aleksander Botev and M. Bauer and Soham De},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.03304}
}
Data augmentation is used in machine learning to make the classifier invariant to label-preserving transformations. Usually this invariance is only encouraged implicitly by sampling a single augmentation per image and training epoch. However, several works have recently shown that using multiple augmentations per input can improve generalisation or can be used to incorporate invariances more explicitly. In this work, we first empirically compare these recently proposed objectives that differ in… 

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