@ptrblck, Thank you for your reply!
Unfortunately, my main goal is to test on the raw dataset, so it is not possible to change the distribution
akin to the validation dataset. My validation data also has similar distribution as the training dataset.
Then my question is, should I also add gaussian noise to my validation dataset or do you still recommend adding noise to generate more data in this case?
I know it would be difficult, but I want to maximize my validation data accuracy as well as accuracy on the raw dataset.
Furthermore, would it be better to train with 1000 original training data + 1000 fixed noised data or 1000 noised data that changes the amount of noise in every epoch? e.g. transforms in torch.vision