Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning

arXiv:2312.11034v3 Announce Type: replace
Abstract: In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. The majority of the existing works focuses on constructing robust classifiers to estimate the labeling confidence of candidate labels in order to identify the correct one. However, these methods usually struggle to identify and rectify mislabeled samples. To help these mislabeled samples "appeal" for themselves and help existing PLL methods identify and rectify mislabeled …

abstract arxiv chance classifiers confidence cs.lg ground-truth instance labeling labels robust samples set truth type