Label Information Bottleneck for Label Enhancement
This work addresses the challenge of label distribution recovery for machine learning tasks, presenting an incremental improvement in label enhancement methods.
The paper tackles the problem of Label Enhancement (LE), which aims to recover label distributions from logical labels, by proposing a Label Information Bottleneck (LIB) method that excavates essential label-relevant information to improve recovery performance, with evaluation on benchmark datasets showing effectiveness and competitiveness.
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