Candidate-aware Selective Disambiguation Based On Normalized Entropy for Instance-dependent Partial-label Learning

In partial-label learning (PLL), each training example has a set of candidate labels, among which only one is the true label. Most existing PLL studies focus on the instance-independent (II) case, where the generation of candidate labels is only dependent on the true label. However, this II-PLL paradigm could be unrealistic, since candidate labels are usually generated according to the specific features of the instance. Therefore, instance-dependent PLL (ID-PLL) has attracted increasing attention recently. Unfortunately, existing ID-PLL studies lack an insightful perception of the intrinsic challenge in ID-PLL. In this paper, we start with an empirical study of the dynamics of label disambiguation in both II-PLL and ID-PLL. We found that the performance degradation of ID-PLL stems from the inaccurate supervision caused by massive under-disambiguated (UD) examples that do not achieve complete disambiguation. To solve this problem, we propose a novel two-stage PLL framework including selective disambiguation and candidate-aware thresholding. Specifically, we first choose a part of well-disambiguated (WD) examples based on the magnitude of normalized entropy (NE) and integrate harmless complementary supervision from the remaining ones to train two networks. Next, the remaining examples whose NE is lower than the specific class-wise WD-NE threshold are selected as additional WD ones. Meanwhile, the remaining UD examples, whose NE is lower than the self-adaptive UD-NE threshold and whose predictions from two networks are agreed, are also regarded as WD ones for model training. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art PLL methods.