I have an unbalanced image dataset with the positive class being 1/10 of the entire dataset. Classification models trained on this dataset tend to be biased toward the majority class (small false negative rate and bigger false positive rate). I would like to do some augmentation only on the minority class to deal with this. The way I understand, using transforms (random rotation, etc.) when creating torchvision.dataset will augment all of the data so the imbalance remains the same.
What is the best way to go about only augmenting images from a specific class?