Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true. In this paper, we introduce ProPaLL, a novel probabilistic approach to this problem, which has at least three advantages compared to the existing approaches: it simplifies the training process, improves performance, and can be applied to any deep architecture. Experiments conducted on artificial and real-world datasets indicate that ProPaLL outperforms the existing approaches.

翻译：部分标签学习是一种监督不力的学习类型,每个培训实例都与一套候选标签相对应,其中只有一种是真实的。 在本文中,我们引入了ProPALL, 这是一种针对这一问题的新颖的概率方法,与现有方法相比至少有三种优势:它简化了培训过程,改进了绩效,并可用于任何深层结构。在人工和真实世界数据集上进行的实验表明,ProPALL优于现有方法。