Robust Noisy Label Learning via Two-Stream Sample Distillation
<|reference_start|>Robust Noisy Label Learning via Two-Stream Sample Distillation: Noisy label learning aims to learn robust networks under the supervision of noisy labels, which plays a critical role in deep learning. Existing work either conducts sample selection or label correction to deal with noisy labels during the model training process. In this paper, we design a simple yet effective sample selection framework, termed Two-Stream Sample Distillation (TSSD), for noisy label learning, which can extract more high-quality samples with clean labels to improve the robustness of network training. Firstly, a novel Parallel Sample Division (PSD) module is designed to generate a certain training set with sufficient reliable positive and negative samples by jointly considering the sample structure in feature space and the human prior in loss space. Secondly, a novel Meta Sample Purification (MSP) module is further designed to mine adequate semi-hard samples from the remaining uncertain training set by learning a strong meta classifier with extra golden data. As a result, more and more high-quality samples will be distilled from the noisy training set to train networks robustly in every iteration. Extensive experiments on four benchmark datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, and Clothing-1M, show that our method has achieved state-of-the-art results over its competitors.<|reference_end|>
@article{bai2024robust,
    title={Robust Noisy Label Learning via Two-Stream Sample Distillation},
    author={Sihan Bai, Sanping Zhou, Zheng Qin, Le Wang, Nanning Zheng},
    journal={arXiv preprint arXiv:2404.10499},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10499},
    primaryClass={cs.CV cs.AI}
}