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.

翻译：部分标签学习（PLL）致力于处理带有模糊标签数据的学习任务，并已成功应用于图像识别等领域。然而，传统PLL方法依赖于封闭世界假设，这限制了其在开放世界场景中的应用，并对模型性能与泛化能力产生负面影响。为应对这些挑战，本研究提出了一种名为PLL-OOD的新方法，这是首次将分布外（OOD）检测融入PLL框架。PLL-OOD通过融合自监督学习与部分标签损失，并首创性地提出用于OOD检测的部分能量（PE）分数，显著提升了模型的适应性与准确性。该方法利用动态标签置信度矩阵优化预测，改进了数据特征表示并有效消歧候选标签。经标签置信度调整后的PE分数能够精准识别OOD实例，从而优化模型在分布内数据上的训练。这一创新方法显著增强了PLL模型在开放世界环境中的鲁棒性与性能。为验证所提方法的有效性，我们在CIFAR-10和CIFAR-100数据集上，结合多种OOD数据集，将现有最优PLL模型与多种OOD分数进行了全面的对比实验。结果表明，所提PLL-OOD框架高效且效果优于现有模型，展现了其优越性与有效性。