TL;DR本文通过研究0-1损失与对手风险之间的单调关系，针对标签污染问题，提出了一种简单高效的课程化损失函数，即Curriculum loss (CL)，用于同时优化深度神经网络的鲁棒性和泛化性。实验结果证明了所提出的损失函数的鲁棒性。
Abstract
generalization is vital important for many deep network models. It becomes more challenging when high robustness is required for learning with noisy labels. The →