Can’t we have uncertain labels for other tasks e.g. multilabel, segmentation, etc…? Or is this idea of label smoothing help with the case of softmax?
It’s not immediately clear to me how to apply label smoothing for segmentation but I’ve been trying it with multilabel with some degree of success. My approach doesn’t have the same degree of mathematical justification but I use a binarizer to create binary indicators for each training sample (1 represents this label is valid for the given example 0 means it is not). I then just modify the 1 to 0.95 and give all the 0’s a small value (eg. 0.001).
My labels don’t sum to 1 because there can be multiple true labels for a given training example. I should also mention that I don’t use fastai for any of this, I just pre-process my .csv or dataframe before feeding it to fastai.