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In partial label learning (PLL), each instance is associated with a set ofcandidate labels among which only one is ground-truth. The majority of theexisting works focuses on constructing robust classifiers to estimate thelabeling confidence of candidate labels in order to identify the correct one.However, these methods usually struggle to rectify mislabeled samples. To helpexisting PLL methods identify and rectify mislabeled samples, in this paper, weintroduce a novel partner classifier and propose a novel ``mutual supervision''paradigm. Specifically, we instantiate the partner classifier predicated on theimplicit fact that non-candidate labels of a sample should not be assigned toit, which is inherently accurate and has not been fully investigated in PLL.Furthermore, a novel collaborative term is formulated to link the baseclassifier and the partner one. During each stage of mutual supervision, bothclassifiers will blur each other's predictions through a blurring mechanism toprevent overconfidence in a specific label. Extensive experiments demonstratethat the performance and disambiguation ability of several well-establishedstand-alone and deep-learning based PLL approaches can be significantlyimproved by coupling with this learning paradigm.

Partial Label Learning with a Partner