FairMatch: Promoting Partial Label Learning by Unlabeled Samples

This paper studies the semi-supervised partial label learning (SSPLL) problem, which aims to improve the partial label learning (PLL) by leveraging unlabeled samples. Both the existing SSPLL methods and the semi-supervised learning methods exploit the information in unlabeled samples by selecting high-confidence unlabeled samples as the pseudo labels based on the maximum value of the model output. However, the scarcity of labeled samples and the ambiguity from partial labels skew this strategy towards an unfair selection of high-confidence samples on each class, most notably during the initial phases of training, resulting in slower training and performance degradation. In this paper, we propose a novel method FairMatch, which adopts a learning state aware self-adaptive threshold for selecting the same number of high-confidence samples on each class, and uses augmentation consistency to incorporate the unlabeled samples to promote PLL. In addition, we adopt the candidate label disambiguation to utilize the partial labeled samples and mix up the partial labeled samples and the selected high-confidence unlabeled samples to prevent the model from overfitting on partial label samples. FairMatch can achieve maximum accuracy improvements of 9.53%, 4.9%, and 16.45% on CIFAR-10, CIFAR-100, and CIFAR-100H, respectively. The codes can be found at https://github.com/jhjiangSEU/FairMatch.