  It has been hypothesized that label smoothing can reduce overfitting and
improve generalization, and current empirical evidence seems to corroborate
these effects. However, there is a lack of mathematical understanding of when
and why such empirical improvements occur. In this paper, as a step towards
understanding why label smoothing is effective, we propose a theoretical
framework to show how label smoothing provides in controlling the
generalization loss. In particular, we show that this benefit can be precisely
formulated and identified in the label noise setting, where the training is
partially mislabeled. Our theory also predicts the existence of an optimal
label smoothing point, a single value for the label smoothing hyperparameter
that minimizes generalization loss. Extensive experiments are done to confirm
the predictions of our theory. We believe that our findings will help both
theoreticians and practitioners understand label smoothing, and better apply
them to real-world datasets.
