Title: Dynamic Loss for Learning with Label Noise. Abstract: Label noise is verified seriously harmful to deep neural networks (DNNs). A simple and scalable strategy to handle this problem is to design robust loss functions, which improve generalization in the presence of label noise by reconciling fitting ability with robustness. However, the widely-used static trade-off between the two contradicts the dynamics of DNNs learning with label noise, leading to an inferior performance. Therefore, in this paper, we propose a dynamic loss function to solve this problem. Specifically, DNNs tend to first learn generalized patterns, then gradually overfit label noise. In light of this, we make fitting ability stronger initially, then gradually increase the weight of robustness. Moreover, we let DNNs put more emphasis on easy examples than hard ones at the later stage since the former are correctly labeled with a higher probability, further reducing the negative impact of label noise. Extensive experimental results on various benchmark datasets demonstrate the state-of-the-art performance of our method. We will open-source our code very soon.