10 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Contrastive Label Enhancement
Yifei Wang, Yiyang Zhou, Jihua Zhu +3
Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity. Since it is difficult to directly obtain label distributions, many studies are foc…
cs.LG2020★ 2 cited
Bidirectional Loss Function for Label Enhancement and Distribution Learning
Xinyuan Liu, Jihua Zhu, Qinghai Zheng +3
Label distribution learning (LDL) is an interpretable and general learning paradigm that has been applied in many real-world applications. In contrast to the simple logical vector…
cs.LG2020★ 10 cited
Generalized Label Enhancement with Sample Correlations
Qinghai Zheng, Jihua Zhu, Haoyu Tang +3
Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances. Different from single-label and multi-l…