343 citations · 435 across the 24 of their papers we have counts for
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cs.LG2024★ 1 cited
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
Huafeng Liu, Mengmeng Sheng, Zeren Sun +3
Learning with noisy labels has gained increasing attention because the inevitable imperfect labels in real-world scenarios can substantially hurt the deep model performance. Recent…
cs.LG2023★ 1 cited
A Universal Unbiased Method for Classification from Aggregate Observations
Zixi Wei, Lei Feng, Bo Han +4
In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances,…
cs.LG2023★ 3 cited
A Comprehensive Survey on Source-free Domain Adaptation
Zhiqi Yu, Jingjing Li, Zhekai Du +2
Over the past decade, domain adaptation has become a widely studied branch of transfer learning that aims to improve performance on target domains by leveraging knowledge from the…