Thanks a lot for your response. I will read the suggested articles in the post you’ve mentioned. So in your opinion, do you think that a more easy way to deal with data imbalance in multi-label classification is to calculate the loss with respect to the class weights (in the way you have previously mentioned here: Multi-Label, Multi-Class class imbalance - #2 by ptrblck 3?
Also, is there any point in the post Class imbalance with image segmentation - #3 by An18 1 and the reply to it that could be useful in my case?
One more question is that I cannot use sklearn.utils.class_weight.compute_class_weight to calculate the class weights since the targets are multi-label and in a one-hot coded format, can I?
I really appreciate your consideration.