Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It

Abstract

Label smoothing (LS) is a popular regularisation method for training neural networks as it is effective in improving test accuracy and is simple to implement. ''Hard'' one-hot labels are ''smoothed'' by uniformly distributing probability mass to other classes, reducing overfitting. Prior work has shown that in some cases *LS can degrade selective classification (SC)* -- where the aim is to reject misclassifications using a model's uncertainty. In this work, we first demonstrate empirically across an extended range of large-scale tasks and architectures that LS *consistently* degrades SC. We then address a gap in existing knowledge, providing an *explanation* for this behaviour by analysing logit-level gradients: LS degrades the uncertainty rank ordering of correct vs incorrect predictions by regularising the max logit *more* when a prediction is likely to be correct, and *less* when it is likely to be wrong. This elucidates previously reported experimental results where strong classifiers underperform in SC. We then demonstrate the empirical effectiveness of post-hoc *logit normalisation* for recovering lost SC performance caused by LS. Furthermore, linking back to our gradient analysis, we again provide an explanation for why such normalisation is effective.