Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension

Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels difficult. However, mixup neglects that multiple annotators, e.g., crowdworkers, typically provide class labels.