Multi-instance partial-label learning (MIPL) is a weakly supervised framework that extends the principles of multi-instance learning (MIL) and partial-label learning (PLL) to address the challenges of inexact supervision in both instance and label spaces. However, existing MIPL approaches often suffer from poor calibration, undermining classifier reliability. In this work, we propose a plug-and-play calibratable disambiguation loss (CDL) that simultaneously improves classification accuracy and calibration performance. The loss has two instantiations: the first one calibrates predictions based on probabilities from the candidate label set, while the second one integrates probabilities from both candidate and non-candidate label sets. The proposed CDL can be seamlessly incorporated into existing MIPL and PLL frameworks. We provide a theoretical analysis that establishes the lower bound and regularization properties of CDL, demonstrating its superiority over conventional disambiguation losses. Experimental results on benchmark and real-world datasets confirm that our CDL significantly enhances both classification and calibration performance.

翻译：多示例部分标签学习（MIPL）是一种弱监督学习框架，它融合了多示例学习（MIL）和部分标签学习（PLL）的原理，旨在应对实例空间和标签空间中监督信息不精确的挑战。然而，现有的MIPL方法通常存在校准性能不佳的问题，这削弱了分类器的可靠性。本文提出了一种即插即用的可校准消歧损失（CDL），该损失函数能同时提升分类精度与校准性能。该损失函数有两种具体实现形式：第一种基于候选标签集的概率对预测进行校准；第二种则整合了候选标签集与非候选标签集的概率。所提出的CDL可以无缝集成到现有的MIPL与PLL框架中。我们提供了理论分析，建立了CDL的下界与正则化特性，证明了其相对于传统消歧损失的优越性。在基准数据集和真实世界数据集上的实验结果证实，我们的CDL能显著提升分类性能与校准性能。