• Corpus ID: 249097375

Mitigating Memorization of Noisy Labels via Regularization between Representations

@inproceedings{Cheng2021MitigatingMO,
  title={Mitigating Memorization of Noisy Labels via Regularization between Representations},
  author={Hao Cheng and Zhaowei Zhu and Xing Sun and Yang Liu},
  year={2021}
}
Designing robust loss functions is popular in learning with noisy labels while existing designs did not explicitly consider the overfitting property of deep neural networks (DNNs). As a result, applying these losses may still suffer from overfitting/memorizing noisy labels as training proceeds. In this paper, we first theoretically analyze the memorization effect and show that a lower-capacity model may perform better on noisy datasets. However, it is non-trivial to design a neural network with the… 

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