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
this https URL

本文研究标签增强问题，提出了一种新的标签信息瓶颈方法来从逻辑标签精确恢复标签分布。该方法通过挖掘关键的标签相关信息来提高恢复性能，并在多个基准标签分布学习数据集上进行实验来验证其有效性和竞争力。