Loss for each label in multi-label classification

[moreshud]

Hi @KFrank,

Your clarification will be appreciated on a similar topic which you had answered in the past.

Same output when using Multilabel BCE

Hi @KFrank Firstly, I would like to appreciate your explanations and contributions. However, I would be glad for the affirmation of gained knowledge from the thread and likewise some clarifications. The difference between single label and multi-label multi-class segmentation is the presence overlapping - a case where particular corresponding pixel value(s) for each target classes is/are annotated The go loss function for single label multi-class is CrossEntropyLoss while for multi-label multi…