In the context of my thesis, I'm currently researching on Label Smoothing.

Does anyone know of any papers or other articles on applying label smoothing not generalized to all training data, but only to specific data?

Thanks!

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EDIT:

The basis of the thesis is a project that focuses on the classification of exact vehicle models. The results of the model are basically good. However, in some cases it is difficult to clearly identify the vehicle model - i.e. there is no clear ground truth. For example, it may not be possible to distinguish whether a car photographed from the front or the rear is a station wagon or a sedan, or whether it is a convertible or a coupe. Currently, my model gives an incorrect prediction with a high probability for such an inconclusive ground truth, indicating that the DNN has little or no uncertainty and is not well calibrated.

A possible approach would be to apply label smoothing, but not generalized to all training data, but only targeted to specific data and specifically to those with inconclusive ground truth, to give/train the model more uncertainty in these situation.