渐进式净化用于实例相关部分标签学习
Progressive Purification for Instance-Dependent Partial Label Learning
AI总结：
针对实例相关部分标签学习，提出渐进式净化方法POP，通过逐步净化候选标签集并更新模型，理论上逼近贝叶斯最优分类器，实验验证其有效性。
摘要：
部分标签学习（PLL）旨在从每个样本都标注有一组候选标签（其中有一个固定但未知的候选标签是正确的）的示例中训练多分类器。在过去几年中，候选标签的实例无关生成过程已被广泛研究，在此基础上，PLL领域取得了许多理论进展。然而，在实践中，候选标签总是实例相关的，并且没有理论保证在实例相关的PLL示例上训练的模型能够收敛到理想模型。本文提出了一种具有理论依据且实际有效的方法，名为POP，即用于实例相关部分标签学习的渐进式净化（PrOgressive Purification）。具体来说，POP在每个epoch中逐步更新学习模型并净化每个候选标签集。理论上，我们证明了POP以适当快的速度扩大模型可靠的区域，并最终在温和假设下逼近贝叶斯最优分类器。技术上，POP与任意PLL损失函数兼容，并且能够在实例相关的情况下提升先前PLL损失函数的性能。在基准数据集和真实世界数据集上的实验验证了所提出方法的有效性。
英文摘要：
Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct. In the last few years, the instance-independent generation process of candidate labels has been extensively studied, on the basis of which many theoretical advances have been made in PLL. Nevertheless, the candidate labels are always instance-dependent in practice and there is no theoretical guarantee that the model trained on the instance-dependent PLL examples can converge to an ideal one. In this paper, a theoretically grounded and practically effective approach named POP, i.e. PrOgressive Purification for instance-dependent partial label learning, is proposed. Specifically, POP updates the learning model and purifies each candidate label set progressively in every epoch. Theoretically, we prove that POP enlarges the region appropriately fast where the model is reliable, and eventually approximates the Bayes optimal classifier with mild assumptions. Technically, POP is flexible with arbitrary PLL losses and could improve the performance of the previous PLL losses in the instance-dependent case. Experiments on the benchmark datasets and the real-world datasets validate the effectiveness of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
