Label noise poses a serious threat to deep neural networks (DNNs). Employing robust loss function which reconciles fitting ability with robustness is a simple but effective strategy to handle this problem. However, the widely-used static trade-off between these two factors contradicts the dynamic nature of DNNs learning with label noise, leading to inferior performance. Therefore, we propose a dynamics-aware loss (DAL) to solve this problem. Considering that DNNs tend to first learn generalized patterns, then gradually overfit label noise, DAL strengthens the fitting ability initially, then gradually increases the weight of robustness. Moreover, at the later stage, we let DNNs put more emphasis on easy examples which are more likely to be correctly labeled than hard ones and introduce a bootstrapping term to further reduce the negative impact of label noise. Both the detailed theoretical analyses and extensive experimental results demonstrate the superiority of our method.

翻译：标签噪声对深度神经网络构成了严重威胁。采用兼顾拟合能力与鲁棒性的稳健损失函数是处理该问题的一种简单而有效的策略。然而，广泛使用的静态权衡机制与深度神经网络在标签噪声下学习的动态特性相矛盾，导致性能欠佳。为此，我们提出了一种动态感知损失函数来解决该问题。考虑到深度神经网络倾向于先学习通用模式，随后逐渐过拟合标签噪声，DAL在初始阶段增强拟合能力，随后逐步提升鲁棒性权重。此外，在后期阶段，我们让深度神经网络更加关注更可能被正确标注的简单样本（而非困难样本），并引入自举项以进一步降低标签噪声的负面影响。详尽的理论分析与广泛的实验结果均证明了本方法的优越性。