Partial Multi-label Learning (PML) tackles the problem where each training instance is associated with a set of candidate labels that include both the relevant ground-truth labels and irrelevant false positive labels. Most of the existing PML methods try to iteratively update the confidence of each candidate label, while the estimated label confidence may be not reliable due to the cumulative error induced in the confidence updating process, especially when false positive labels dominate. In this paper, we propose a simple yet effective model called PML-MT (Partial Multi-label Learning with Mutual Teaching), in which a couple of prediction networks as well as the corresponding teacher networks are adopted to learn collaboratively and teach each other throughout the training process. Specially, the proposed PML-MT model iteratively refines the label confidence matrix through a couple of self-ensemble teacher networks and trains two prediction networks simultaneously in a mutual teaching manner. Moreover, we propose a novel regularization term to further exploit label correlations from the outputs of the prediction networks under the supervision of the refined label confidence matrix. In addition, a co-regularization term is introduced to maximize the agreement on the outputs of the couple prediction networks, so that the predictions of each network would be more reliable. Extensive experiments on synthesized and real-world PML datasets demonstrate that the proposed approach outperforms the state-of-the-art counterparts.

部分多标签学习（PML）解决了每个训练实例都与一组候选标签相关联的问题，该候选标签包括相关的真实标签和不相关的假阳性标签。大多数现有的PML方法都尝试迭代更新每个候选标签的置信度，而估计的标签置信度可能由于在置信度更新过程中引起的累积误差而不可靠，尤其是在误报标签占主导地位时。在本文中，我们提出了一个简单而有效的模型，称为PML-MT（带有相互教学的部分多标签学习），其中采用了两个预测网络以及相应的教师网络来进行协作学习和相互教学培训过程。特别，提出的PML-MT模型通过几个自律式教师网络迭代地完善了标签置信矩阵，并以相互的教学方式同时训练了两个预测网络。此外，我们提出了一个新的正则化项，以在改进的标签置信度矩阵的监督下进一步利用预测网络的输出中的标签相关性。另外，引入了共正则化术语以最大化对夫妇预测网络的输出的一致性，从而每个网络的预测将更加可靠。在合成的和真实的PML数据集上进行的大量实验表明，该方法优于最新的方法。