Regularly Truncated M-estimators for Learning with Noisy Labels

• URL: http://arxiv.org/abs/2309.00894v1
• Date: Sat, 2 Sep 2023 10:22:20 GMT
• Title: Regularly Truncated M-estimators for Learning with Noisy Labels
• Authors: Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu
• Abstract summary: 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. We demonstrate that our strategies are label-noise-tolerant.
• Score: 79.36560434324586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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