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
 A: I think there is no "one-size-fits-all" answer here.
I use repeated CV always so the question is almost a non-issue in that scenario in my opinion. Not having a single validation fold, if anything allows us to gauge how variable our learner's performance is. So, yes, absolutely I would use data augmentation on the validation set as I see the validation and training set as a natural extension of one another in the case of repeated CV.
The above being said if we notice that we have issues of overfitting one of the things we should investigate is the data augmentation process, on the validation set and as a whole, as it could be the case that we are learning how to generalise against the dataset's augmentation procedure rather than the dataset's generative procedure. Similarly, if we have a single/static validation set, it makes sense not to use augmented data points in it as we will have no way to know if we are biasing our training procedure's validation.
Finally do note that by data augmentation we do not have a procedure that alters the specific prevalence of class in our validation set. We should maintain similar class proportions otherwise we will obviously have skewed out-of-sample performance.
A: 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.
A: 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.
