Jo-SNC: Combating Noisy Labels Through Fostering Self- and Neighbor-Consistency
Sun, Zeren Yao, Yazhou Liu, Tongliang Li, Zechao Shen, Fumin Tang, Jinhui
Sun, Zeren
Yao, Yazhou
Liu, Tongliang
Li, Zechao
Shen, Fumin
Tang, Jinhui
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Machine Learning
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Journal article
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English
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Abstract
Label noise is pervasive in various real-world scenarios, posing challenges in supervised deep learning. Deep networks are vulnerable to such label-corrupted samples due to the memorization effect. One major stream of previous methods concentrates on identifying clean data for training. However, these methods often neglect imbalances in label noise across different mini-batches and devote insufficient attention to out-of-distribution noisy data. To this end, we propose a noise-robust method named Jo-SNC (Joint sample selection and model regularization based on Self- and Neighbor-Consistency). Specifically, we propose to employ the Jensen-Shannon divergence to measure the "likelihood" of a sample being clean or out-of-distribution. This process factors in the nearest neighbors of each sample to reinforce the reliability of clean sample identification. We design a self-adaptive, data-driven thresholding scheme to adjust per-class selection thresholds. While clean samples undergo conventional training, detected in-distribution and out-of-distribution noisy samples are trained following partial label learning and negative learning, respectively. Finally, we advance the model performance further by proposing a triplet consistency regularization that promotes self-prediction consistency, neighbor-prediction consistency, and feature consistency. Extensive experiments on various benchmark datasets and comprehensive ablation studies demonstrate the effectiveness and superiority of our approach over existing state-of-the-art methods.
Citation
Z. Sun, Y. Yao, T. Liu, Z. Li, F. Shen, J. Tang, "Jo-SNC: Combating Noisy Labels Through Fostering Self- and Neighbor-Consistency," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 48, no. 04, pp. 4708-4725, 2025, https://doi.org/10.1109/tpami.2025.3646737.
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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46 Information and Computing Sciences, 4611 Machine Learning
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IEEE