9 citations · 24 across the 12 of their papers we have counts for
Showing 2021Show all
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cs.LG2021★ 7 cited
Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
Mingcai Chen, Hao Cheng, Yuntao Du +3
Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pipeline alternates between eliminating possible incorrect labels and semi-supervis…
cs.LG2021★ 9 cited
Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation
Yuntao Du, Haiyang Yang, Mingcai Chen +3
Conventional unsupervised domain adaptation (UDA) methods need to access both labeled source samples and unlabeled target samples simultaneously to train the model. While in some s…
cs.LG2021★ 2 cited
Semi-Supervised Learning with Multi-Head Co-Training
Mingcai Chen, Yuntao Du, Yi Zhang +2
Co-training, extended from self-training, is one of the frameworks for semi-supervised learning. Without natural split of features, single-view co-training works at the cost of tra…