Masaki Kashiwagi

Enhancing Noisy Label Learning Via Unsupervised Contrastive Loss with Label Correction Based on Prior Knowledge

To alleviate the negative impacts of noisy labels, most of the noisy label learning (NLL) methods dynamically divide the training data into two types, "clean samples" and "noisy samples", in the training process. However, the conventional selection of clean samples heavily depends on the features le…