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

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 suggested 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 suppressing 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. Project page: https://ensta-u2is-ai.github. io/Understanding-Label-smoothing-Selective-classification/ Recently Zhu et al. (2022; 2024) empirically observe that LS can lead to worse SC for convolutional neural networks (CNNs) performing image classification, yet it is not clear why this occurs. Concurrently, large-scale empirical evaluations of open-source pre-trained models have shown