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Self-label correction for image classification with noisy labels

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Abstract

Label noise is inevitable in image classification. Existing methods usually lack the reliability of selecting clean data samples and rely on an auxiliary model to correct clean samples, which quality has a great impact on the classification results. In this paper, we propose the Dual-model and Self-Label Correction (DSLC) method to select clean samples and correct labels without auxiliary models. First, we use a dual-model structure combining contrastive learning to select clean samples. Then, we design a novel label correction method to modify the noisy labels. Finally, we propose a joint loss to improve the generalization ability of our models. In the experiment, we demonstrate the effectiveness of DSLC on various datasets, which achieves comparable performance to state-of-the-art methods.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2018AAA0100104, 2018AAA0100100) and Natural Science Foundation of Jiangsu Province (BK20211164). We thank the Big Data Computing Center of Southeast University for providing the facility support on the numerical calculations in this paper.

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Authors and Affiliations

  1. School of Computer Science and Engineering, The Key Lab of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing, 211189, China

    Yu Zhang & Fan Lin

  2. School of Cyber Science and Engineering, Southeast University, Nanjing, 211189, China

    Siya Mi

  3. Purple Mountain Laboratories, Nanjing, 211111, China

    Siya Mi

  4. Intel Labs, Santa Clara, USA

    Yali Bian

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  1. Yu Zhang
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  2. Fan Lin
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  3. Siya Mi
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  4. Yali Bian
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Correspondence to Yu Zhang.

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Zhang, Y., Lin, F., Mi, S. et al. Self-label correction for image classification with noisy labels. Pattern Anal Applic 26, 1505–1514 (2023). https://doi.org/10.1007/s10044-023-01180-w

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