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:

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*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

I'd say no. When validating the model you want to estimate its potential performance in the real world. Validating it on synthetic data would work only if the synthetic data is exactly like the data you would find in the wild, but do you ever have such guarantees?
There are different kinds of data augmentation. Things like adding extra noise to images, or rotating them, do not sound controversial from the above point of view, but in such a case you would have the same image repeated in the validation set multiple times, or even worse you would have a different version of the image spread between train and validation sets (data leak). This would bias your results unless you alter the image and throw away the raw image. In most cases, I'd prefer to validate on raw data than on artificially altered data if I needed to choose.
So it is a risky strategy, that can give you an unrealistic estimate of the model performance. Though agree with @usεr11852, there may be cases where you have no better alternatives and poor validation data is better than none.
