Partial Label Learning (PLL) grapples with learning from ambiguously labelled
data, and it has been successfully applied in fields such as image recognition.
Nevertheless, traditional PLL methods rely on the closed-world assumption,
which can be limiting in open-world scenarios and negatively impact model
performance and generalization. To tackle these challenges, our study
introduces a novel method called PLL-OOD, which is the first to incorporate
Out-of-Distribution (OOD) detection into the PLL framework. PLL-OOD
significantly enhances model adaptability and accuracy by merging
self-supervised learning with partial label loss and pioneering the
Partial-Energy (PE) score for OOD detection. This approach improves data
feature representation and effectively disambiguates candidate labels, using a
dynamic label confidence matrix to refine predictions. The PE score, adjusted
by label confidence, precisely identifies OOD instances, optimizing model
training towards in-distribution data. This innovative method markedly boosts
PLL model robustness and performance in open-world settings. To validate our
approach, we conducted a comprehensive comparative experiment combining the
existing state-of-the-art PLL model with multiple OOD scores on the CIFAR-10
and CIFAR-100 datasets with various OOD datasets. The results demonstrate that
the proposed PLL-OOD framework is highly effective and effectiveness
outperforms existing models, showcasing its superiority and effectiveness.

我们的研究引入了一种名为 PLL-OOD 的新方法，将 Out-of-Distribution（OOD）检测纳入 Partial Label Learning（PLL）框架，通过合并自我监督学习和部分标签损失以及 PE 得分，实现了模型的适应性和准确性的显著提升，在开放世界环境中大大提高了 PLL 模型的鲁棒性和性能。