62 citations · 113 across the 15 of their papers we have counts for
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cs.LG2021★ 62 cited
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
Jiaheng Wei, Zhaowei Zhu, Hao Cheng +3
Existing research on learning with noisy labels mainly focuses on synthetic label noise. Synthetic noise, though has clean structures which greatly enabled statistical analyses, of…
cs.LG2021★ 16 cited
To Smooth or Not? When Label Smoothing Meets Noisy Labels
Jiaheng Wei, Hangyu Liu, Tongliang Liu +3
Label smoothing (LS) is an arising learning paradigm that uses the positively weighted average of both the hard training labels and uniformly distributed soft labels. It was shown…
cs.LG2021★ 1 cited
DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training
Jiaheng Wei, Minghao Liu, Jiahao Luo +3
In this paper, we introduce DuelGAN, a generative adversarial network (GAN) solution to improve the stability of the generated samples and to mitigate mode collapse. Built upon the…