In the standard label smoothing regime, label smoothing is applied to every training example. I have a real world dataset where there is some label noise between 2 of the 30 classes I’m training the classifier on. Basically, two of the classes may be mislabeled as one another. I was hoping to try to use label smoothing as a way to compensate for some of the noise in these labels.
Does anyone have an idea of how to implement this efficiently?