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

arXiv:2405.03386v1 Announce Type: new
Abstract: 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 more difficult. However, mixup neglects that, typically, multiple annotators, e.g., crowdworkers, provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs …

arxiv class cs.lg extension labels multiple type via