Hello!
Thank you for your answer!
I tried to follow your first direction but from what I understand this approach would help me in balancing examples between the train and test sets.
What I am trying to achieve is to obtain a well balanced train set, without focusing on the test one at all.
My goal would be to sample balanced batches from the training dataset (carrying out data augmentation only on the minority class since this examples would theoretically get picked more).
Per epoch the idea would be to see all minority examples multiple times (with different transforms applied) and only once the majority examples. Does that make sense?
For the second direction you proposed, i don’t see how it would help in this situation. Could you expand on the topic please?
Again, thank you for your response!