In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demonstrate a good ability to estimate accurate confidence scores from candidate labels, have been prevalent to deal with PML problems. However, we observe that existing graph-based PML methods typically adopt linear multi-label classifiers and thus fail to achieve superior performance. In this work, we attempt to remove several obstacles for extending them to deep models and propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN). Specifically, we introduce the instance-level and label-level similarities to recover label confidences as well as exploit label dependencies. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels; then, we train the deep model to fit the numerical labels. Moreover, we provide a careful analysis of the risk functions to guarantee the robustness of the proposed model. Extensive experiments on various synthetic datasets and three real-world PML datasets demonstrate that PLAIN achieves significantly superior results to state-of-the-art methods.

翻译：在部分多标签学习（PML）中，每个数据样本配备一个候选标签集，该集合包含多个真实标签和其他假阳性标签。近年来，基于图的方法在从候选标签中估计准确的置信度分数方面展现出良好能力，因此被广泛用于处理PML问题。然而，我们观察到现有的基于图的PML方法通常采用线性多标签分类器，因此无法取得优越性能。本文尝试移除将其扩展到深度模型的若干障碍，并提出一种新颖的基于图消歧的深度部分多标签模型（PLAIN）。具体而言，我们引入实例级和标签级相似度来恢复标签置信度，同时利用标签依赖关系。在每个训练周期，标签在实例图和标签图上传播以生成相对准确的伪标签；随后，我们训练深度模型拟合这些数值标签。此外，我们对风险函数进行了仔细分析，以保证所提模型的鲁棒性。在各种合成数据集和三个真实世界PML数据集上的大量实验表明，PLAIN相比现有最优方法取得了显著更优的结果。