Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity
<|reference_start|>Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity: Real-world datasets commonly exhibit noisy labels and class imbalance, such as long-tailed distributions. While previous research addresses this issue by differentiating noisy and clean samples, reliance on information from predictions based on noisy long-tailed data introduces potential errors. To overcome the limitations of prior works, we introduce an effective two-stage approach by combining soft-label refurbishing with multi-expert ensemble learning. In the first stage of robust soft label refurbishing, we acquire unbiased features through contrastive learning, making preliminary predictions using a classifier trained with a carefully designed BAlanced Noise-tolerant Cross-entropy (BANC) loss. In the second stage, our label refurbishment method is applied to obtain soft labels for multi-expert ensemble learning, providing a principled solution to the long-tail noisy label problem. Experiments conducted across multiple benchmarks validate the superiority of our approach, Label Refurbishment considering Label Rarity (LR^2), achieving remarkable accuracies of 94.19% and 77.05% on simulated noisy CIFAR-10 and CIFAR-100 long-tail datasets, as well as 77.74% and 81.40% on real-noise long-tail datasets, Food-101N and Animal-10N, surpassing existing state-of-the-art methods.<|reference_end|>
@article{wu2024addressing,
    title={Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution
  with Label Refurbishment Considering Label Rarity},
    author={Ying-Hsuan Wu, Jun-Wei Hsieh, Li Xin, Shin-You Teng, Yi-Kuan Hsieh,
  Ming-Ching Chang},
    journal={arXiv preprint arXiv:2403.02363},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02363},
    primaryClass={cs.LG cs.AI}
}