Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid. In recent years, benefiting from the strong capability of dealing with ambiguous supervision and the impetus of modern data augmentation methods, consistency regularization-based PLL methods have achieved a series of successes and become mainstream. However, as the partial annotation becomes insufficient, their performances drop significantly. In this paper, we leverage easily accessible unlabeled examples to facilitate the partial label consistency regularization. In addition to a partial supervised loss, our method performs a controller-guided consistency regularization at both the label-level and representation-level with the help of unlabeled data. To minimize the disadvantages of insufficient capabilities of the initial supervised model, we use the controller to estimate the confidence of each current prediction to guide the subsequent consistency regularization. Furthermore, we dynamically adjust the confidence thresholds so that the number of samples of each class participating in consistency regularization remains roughly equal to alleviate the problem of class-imbalance. Experiments show that our method achieves satisfactory performances in more practical situations, and its modules can be applied to existing PLL methods to enhance their capabilities.

翻译：部分标签学习（PLL）从每个关联多个候选标签的训练样本中学习，其中仅有一个标签是有效的。近年来，受益于处理模糊监督的强能力以及现代数据增强方法的推动，基于一致性正则化的PLL方法取得了一系列成功并成为主流。然而，当部分标注变得不充分时，这些方法的性能会显著下降。本文中，我们利用易于获取的无标签样本来促进部分标签一致性正则化。除了部分监督损失外，我们的方法在无标签数据的辅助下，在标签层面和表示层面均执行控制器引导的一致性正则化。为减轻初始监督模型能力不足的劣势，我们使用控制器估计当前每个预测的置信度，以指导后续的一致性正则化。此外，我们动态调整置信度阈值，使参与一致性正则化的各类样本数量大致保持相等，从而缓解类别不平衡问题。实验表明，我们的方法在更实际的场景中取得了令人满意的性能，且其模块可应用于现有PLL方法以增强其能力。