Web: http://arxiv.org/abs/2209.10365

Sept. 22, 2022, 1:14 a.m. | Haobo Wang, Mingxuan Xia, Yixuan Li, Yuren Mao, Lei Feng, Gang Chen, Junbo Zhao

cs.CV updates on arXiv.org arxiv.org

Partial-label learning (PLL) is a peculiar weakly-supervised learning task
where the training samples are generally associated with a set of candidate
labels instead of single ground truth. While a variety of label disambiguation
methods have been proposed in this domain, they normally assume a
class-balanced scenario that may not hold in many real-world applications.
Empirically, we observe degenerated performance of the prior methods when
facing the combinatorial challenge from the long-tailed distribution and
partial-labeling. In this work, we first identify …

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