Curious, do people use data augmentation on the validation set? I am aware there is a debate for the test set -- but the validation set is usually a split form the train set, so wouldn't it make sense to use data augmentation for that?
Also, augmentation helps for models to be better, so wouldn't it make more sense to have the val set version that might improve the model most? (e.g. if you are doing early stopping)
Note: this is not the same as asking to augment the test set. The test should never be used during the ML cycles, only to report values on a paper.
Related:
- Data augmentation on training set only?
- https://github.com/learnables/learn2learn/issues/309
- https://stackoverflow.com/questions/48029542/data-augmentation-in-test-validation-set
- https://www.reddit.com/r/learnmachinelearning/comments/sqxb3i/does_it_make_sense_to_use_data_augmentation_on/
- https://www.quora.com/unanswered/Does-it-make-sense-to-use-data-augmentation-on-the-Validation-set-note-this-is-not-the-same-as-asking-to-augment-the-test-set
