Thank you very much @ptrblck. This will apply random transformations on the images within the minority class without physically changing the size of the minority class, right? I mean, if my dataset has 100 images, 90 of one class and 10 in the other class, I will get the effect of augmentation by iterating many times over it and not by physically having a 90:90 balanced set where 80 minority images were added through transformations. Am I right?
Also, oversampling through WeightedRandomSampler just adds exact copies of the minority to reach the target balanced weights, correct?