Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning

20 Jun 2020Ximing LiYang Wang

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, \ie the likelihood of a candidate label being a ground-truth one... (read more)

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