Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise

• URL: http://arxiv.org/abs/2112.02960v1
• Date: Mon, 6 Dec 2021 12:10:17 GMT
• Title: Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
• Authors: Mingcai Chen, Hao Cheng, Yuntao Du, Ming Xu, Wenyu Jiang, Chongjun Wang
• Abstract summary: Robust Label Refurbishment (Robust LR) is a new hybrid method that integrates pseudo-labeling and confidence estimation techniques to refurbish noisy labels. We show that our method successfully alleviates the damage of both label noise and confirmation bias. For example, Robust LR achieves up to 4.5% absolute top-1 accuracy improvement over the previous best on the real-world noisy dataset WebVision.
• Score: 6.303101074386922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pipeline alternates between eliminating possible incorrect labels and semi-supervised training. However, discarding part of observed labels could result in a loss of information, especially when the corruption is not completely random, e.g., class-dependent or instance-dependent. Moreover, from the training dynamics of a representative two-stage method DivideMix, we identify the domination of confirmation bias: Pseudo-labels fail to correct a considerable amount of noisy labels and consequently, the errors accumulate. To sufficiently exploit information from observed labels and mitigate wrong corrections, we propose Robust Label Refurbishment (Robust LR)-a new hybrid method that integrates pseudo-labeling and confidence estimation techniques to refurbish noisy labels. We show that our method successfully alleviates the damage of both label noise and confirmation bias. As a result, it achieves state-of-the-art results across datasets and noise types. For example, Robust LR achieves up to 4.5% absolute top-1 accuracy improvement over the previous best on the real-world noisy dataset WebVision.

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