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) 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

You can use augmentation data in training, validation and test sets.
The only thing to avoid is using the same data from the training set in validation or test sets.
For example, if you generate 3 augmented instances from an register took from the training data, make sure that no one of these 3 augmented instances accidentally ends up in the validation or test sets.
It turns out that using data from the training set, even augmented data, to validate or test a model is a methodology mistake providing a false generalisation model performance.
