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cs.LG2026★ 15 cited
Variational Rectification Inference for Learning with Noisy Labels
Haoliang Sun, Qi Wei, Lei Feng +4
Label noise has been broadly observed in real-world datasets. To mitigate the negative impact of overfitting to label noise for deep models, effective strategies (\textit{e.g.}, re…
cs.LG2026
FastBUS: A Fast Bayesian Framework for Unified Weakly-Supervised Learning
Ziquan Wang, Haobo Wang, Ke Chen +2
Machine Learning often involves various imprecise labels, leading to diverse weakly supervised settings. While recent methods aim for universal handling, they usually suffer from c…
cs.LG2024
Understanding and Mitigating the Bias in Sample Selection for Learning with Noisy Labels
Qi Wei, Lei Feng, Haobo Wang +1
Learning with noisy labels aims to ensure model generalization given a label-corrupted training set. The sample selection strategy achieves promising performance by selecting a lab…