In my experience it is quite commonplace to alter the loss function used when training a neural network for segmentation to ignore the contribution to the loss of unlabelled pixels. There are a few ways to implement it, but the one I have used prior is to alter network predictions to always predict truly unlabelled pixels correctly. In psuedo-code
predicted[where(labels == unlabelled)] = unlabelled
Is there a common, or most common, term for this?
I am attempting to add the technique itself as part of a literature review, though I can't find any examples which "directly" reference the technique. I can really only find articles that use the technique, but really make no mention of it.