论文标题
依赖实例的部分标签学习的分解生成过程
Decompositional Generation Process for Instance-Dependent Partial Label Learning
论文作者
论文摘要
部分标签学习（PLL）是一个典型的弱监督学习问题，每个培训示例都与一组候选标签相关联，其中只有一个是真实的。大多数现有的PLL方法都假定每个训练示例中的错误标签被随机选择为候选标签，并以简单的方式对候选标签的生成过程进行建模。但是，由于候选标签的生成过程始终取决于实例，这些方法通常不如预期的。因此，应该以精致的方式对其进行建模。在本文中，我们考虑了依赖实例的PLL，并假设候选标签的生成过程可以分解为两个顺序的部分，在该部分中，正确的标签首先出现在注释符的心中，但由于标签不确定的标签而导致与正确标签相关的错误标签作为候选标签。在此考虑的过程中，我们提出了一种新型的PLL方法，该方法通过分解的概率分布模型，基于候选标签的明确建模的生成过程执行最大后验（MAP）。对手动损坏的基准数据集和现实数据集进行了广泛的实验验证了所提出方法的有效性。源代码可从https://github.com/palm-ml/idgp获得。
Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the candidate labels in a simple way. However, these approaches usually do not perform as well as expected due to the fact that the generation process of the candidate labels is always instance-dependent. Therefore, it deserves to be modeled in a refined way. In this paper, we consider instance-dependent PLL and assume that the generation process of the candidate labels could decompose into two sequential parts, where the correct label emerges first in the mind of the annotator but then the incorrect labels related to the feature are also selected with the correct label as candidate labels due to uncertainty of labeling. Motivated by this consideration, we propose a novel PLL method that performs Maximum A Posterior (MAP) based on an explicitly modeled generation process of candidate labels via decomposed probability distribution models. Extensive experiments on manually corrupted benchmark datasets and real-world datasets validate the effectiveness of the proposed method. Source code is available at https://github.com/palm-ml/idgp.
