Coordinated Sparse Recovery of Label Noise
<|reference_start|>Coordinated Sparse Recovery of Label Noise: Label noise is a common issue in real-world datasets that inevitably impacts the generalization of models. This study focuses on robust classification tasks where the label noise is instance-dependent. Estimating the transition matrix accurately in this task is challenging, and methods based on sample selection often exhibit confirmation bias to varying degrees. Sparse over-parameterized training (SOP) has been theoretically effective in estimating and recovering label noise, offering a novel solution for noise-label learning. However, this study empirically observes and verifies a technical flaw of SOP: the lack of coordination between model predictions and noise recovery leads to increased generalization error. To address this, we propose a method called Coordinated Sparse Recovery (CSR). CSR introduces a collaboration matrix and confidence weights to coordinate model predictions and noise recovery, reducing error leakage. Based on CSR, this study designs a joint sample selection strategy and constructs a comprehensive and powerful learning framework called CSR+. CSR+ significantly reduces confirmation bias, especially for datasets with more classes and a high proportion of instance-specific noise. Experimental results on simulated and real-world noisy datasets demonstrate that both CSR and CSR+ achieve outstanding performance compared to methods at the same level.<|reference_end|>
@article{yang2024coordinated,
    title={Coordinated Sparse Recovery of Label Noise},
    author={Yukun Yang, Naihao Wang, Haixin Yang, Ruirui Li},
    journal={arXiv preprint arXiv:2404.04800},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04800},
    primaryClass={cs.LG cs.CV stat.ML}
}