Regularly Truncated M-estimators for Learning with Noisy Labels

1 code implementation • 2 Sep 2023 • Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Tongliang Liu

However, such a procedure is arguably debatable from two folds: (a) it does not consider the bad influence of noisy labels in selected small-loss examples; (b) it does not make good use of the discarded large-loss examples, which may be clean or have meaningful information for generalization.

Learning with noisy labels