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Label noise poses a serious threat to deep neural networks (DNNs). Employingrobust loss functions which reconcile fitting ability with robustness is asimple but effective strategy to handle this problem. However, the widely-usedstatic trade-off between these two factors contradicts the dynamics of DNNslearning with label noise, leading to inferior performance. Therefore, wepropose a dynamics-aware loss (DAL) to solve this problem. Considering thatDNNs tend to first learn beneficial patterns, then gradually overfit harmfullabel noise, DAL strengthens the fitting ability initially, then graduallyimproves robustness. Moreover, at the later stage, to further reduce thenegative impact of label noise and combat underfitting simultaneously, we letDNNs put more emphasis on easy examples than hard ones and introduce abootstrapping term. Both the detailed theoretical analyses and extensiveexperimental results demonstrate the superiority of our method. Our source codecan be found in https://github.com/XiuchuanLi/DAL.

Dynamics-Aware Loss for Learning with Label Noise