In this work, we focus on the challenging problem of Label Enhancement (LE), which aims to exactly recover label distributions from logical labels, and present a novel Label Information Bottleneck (LIB) method for LE. For the recovery process of label distributions, the label irrelevant information contained in the dataset may lead to unsatisfactory recovery performance. To address this limitation, we make efforts to excavate the essential label relevant information to improve the recovery performance. Our method formulates the LE problem as the following two joint processes: 1) learning the representation with the essential label relevant information, 2) recovering label distributions based on the learned representation. The label relevant information can be excavated based on the "bottleneck" formed by the learned representation. Significantly, both the label relevant information about the label assignments and the label relevant information about the label gaps can be explored in our method. Evaluation experiments conducted on several benchmark label distribution learning datasets verify the effectiveness and competitiveness of LIB. Our source codes are available https://github.com/qinghai-zheng/LIBLE
翻译:在这项工作中,我们侧重于标签增强(LE)这一具有挑战性的问题,它旨在从逻辑标签中确切地恢复标签分布,并为LE提供一个全新的标签信息布局(LIB)方法。对于标签分发的回收过程,数据集中所含的标签不相关的信息可能导致不令人满意的回收性能。为解决这一限制,我们努力挖掘基本标签相关信息,以改进回收绩效。我们的方法将LE问题表述为以下两个联合程序:(1) 学习基本标签相关信息的表述;(2) 根据所学的表述方式恢复标签分配。根据所学的“botleneck”(LIB),可以挖掘相关的标签信息。重要的是,关于标签分配的相关信息和标签差距的相关信息都可以在我们的方法中加以探讨。在几个基准标签分发学习数据集上进行的评估实验可以核实LB的有效性和竞争力。我们的源代码是 https://github.com/qinghai-zheng/LIFLE。</s>