Teaching with Soft Label Smoothing for Mitigating Noisy Labels in Facial Expressions

Tohar Lukov, Na Zhao, Gim Hee Lee, Ser-Nam Lim

Abstract: Recent studies have highlighted the problem of noisy labels in large scale in-the-wild facial expressions datasets due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. To solve the problem of noisy labels, we propose Soft Label Smoothing (SLS), which smooths out multiple high-confidence classes in the logits by assigning them a probability based on the corresponding confidence, and at the same time assigning a fixed low probability to the low-confidence classes. Specifically, we introduce what we call the Smooth Operator Framework for Teaching (SOFT), based on a mean-teacher (MT) architecture where SLS is applied over the teacher’s logits. We find that the smoothed teacher’s logit provides a beneficial supervision to the student via a consistency loss – at 30% noise rate, SLS leads to 15% reduction in the error rate compared with MT. Overall, SOFT beats the state of the art at mitigating noisy labels by a significant margin for both symmetric and asymmetric noise. Our code is available at https://github.com/toharl/soft .