论文标题
具有非随机标签噪声的部分标签学习的多级生成模型
Multi-Level Generative Models for Partial Label Learning with Non-random Label Noise
论文作者
论文摘要
部分标签（PL）学习解决了每个训练实例与一组候选标签相关联的问题，其中包括真正的标签和无关的噪声标签。在本文中，我们提出了一种新型的部分标签学习多级生成模型（MGPLL），该模型通过在标签矢量和数据样本之间学习标签级对抗发电机和特征水平对抗性生成器来解决问题。具体而言，MGPLL使用有条件的噪声标签生成网络对非随机噪声标签进行建模并执行标签denoising，并使用多级预测器将训练实例映射到已分拆的标签向量，而有条件的数据功能生成器则用于形成从已贬低的标签向量的倒数映射到数据样品。噪声标签生成器和数据功能生成器都以对抗性方式学习，以分别匹配观察到的候选标签和数据功能。广泛的实验是对合成和现实世界标签数据集进行的。提出的方法证明了部分标签学习的最新性能。
Partial label (PL) learning tackles the problem where each training instance is associated with a set of candidate labels that include both the true label and irrelevant noise labels. In this paper, we propose a novel multi-level generative model for partial label learning (MGPLL), which tackles the problem by learning both a label level adversarial generator and a feature level adversarial generator under a bi-directional mapping framework between the label vectors and the data samples. Specifically, MGPLL uses a conditional noise label generation network to model the non-random noise labels and perform label denoising, and uses a multi-class predictor to map the training instances to the denoised label vectors, while a conditional data feature generator is used to form an inverse mapping from the denoised label vectors to data samples. Both the noise label generator and the data feature generator are learned in an adversarial manner to match the observed candidate labels and data features respectively. Extensive experiments are conducted on synthesized and real-world partial label datasets. The proposed approach demonstrates the state-of-the-art performance for partial label learning.
