IRNet: Iterative Refinement Network for Noisy Partial Label Learning

Partial label learning (PLL) is a typical weakly supervised learning, where each sample is associated with a set of candidate labels. Its basic assumption is that the ground-truth label must be in the candidate set, but this assumption may not be satisfied due to the unprofessional judgment of annot...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník PP; s. 1 - 17
Hlavní autori:	Lian, Zheng, Xu, Mingyu, Chen, Lan, Sun, Licai, Liu, Bin, Feng, Lei, Tao, Jianhua
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 13.10.2025
Predmet:	Annotations Electronic mail Entropy Faces Iterative methods Iterative Refinement Network (IRNet) label correction Maximum likelihood estimation multi-round refinement Noise measurement Noise robustness noisy partial label learning noisy sample detection Phase locked loops Training
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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