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Partial-label learning (PLL) relies on a key assumption that the true labelof each training example must be in the candidate label set. This restrictiveassumption may be violated in complex real-world scenarios, and thus the truelabel of some collected examples could be unexpectedly outside the assignedcandidate label set. In this paper, we term the examples whose true label isoutside the candidate label set OOC (out-of-candidate) examples, and pioneer anew PLL study to learn with OOC examples. We consider two types of OOC examplesin reality, i.e., the closed-set/open-set OOC examples whose true label isinside/outside the known label space. To solve this new PLL problem, we firstcalculate the wooden cross-entropy loss from candidate and non-candidate labelsrespectively, and dynamically differentiate the two types of OOC examples basedon specially designed criteria. Then, for closed-set OOC examples, we conductreversed label disambiguation in the non-candidate label set; for open-set OOCexamples, we leverage them for training by utilizing an effectiveregularization strategy that dynamically assigns random candidate labels fromthe candidate label set. In this way, the two types of OOC examples can bedifferentiated and further leveraged for model training. Extensive experimentsdemonstrate that our proposed method outperforms state-of-the-art PLL methods.

Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples