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arXiv · 2309.00894

Regularly Truncated M-estimators for Learning with Noisy Labels

Xiaobo Xia·Pengqian Lu·Chen Gong·Bo Han·Jun Yu·Tongliang Liu
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Abstract

The sample selection approach is very popular in learning with noisy labels. As deep networks learn pattern first, prior methods built on sample selection share a similar training procedure: the small-loss examples can be regarded as clean examples and used for helping generalization, while the large-loss examples are treated as mislabeled ones and excluded from network parameter updates. However, such a procedure is arguably debatable from two folds: (a) it does not consider the bad influence of noisy labels in selected small-loss examples; (b) it does not make good use of the discarded large-loss examples, which may be clean or have meaningful information for generalization. In this paper, we propose regularly truncated M-estimators (RTME) to address the above two issues simultaneously. Specifically, RTME can alternately switch modes between truncated M-estimators and original M-estimators. The former can adaptively select small-losses examples without knowing the noise rate and reduce the side-effects of noisy labels in them. The latter makes the possibly clean examples but with large losses involved to help generalization. Theoretically, we demonstrate that our strategies are label-noise-tolerant. Empirically, comprehensive experimental results show that our method can outperform multiple baselines and is robust to broad noise types and levels.

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Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Tongliang Liu. 2023-09-02. Regularly Truncated M-estimators for Learning with Noisy Labels. https://arxiv.org/abs/2309.00894

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