2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data
Tong Wei, Qian-Yu Liu, Jiang-Xin Shi +2
Long-Tailed Semi-Supervised Learning (LTSSL) aims to learn from class-imbalanced data where only a few samples are annotated. Existing solutions typically require substantial cost…
cs.LG2020★ 2 cited
Weakly Supervised Learning Meets Ride-Sharing User Experience Enhancement
Lan-Zhe Guo, Feng Kuang, Zhang-Xun Liu +3
Weakly supervised learning aims at coping with scarce labeled data. Previous weakly supervised studies typically assume that there is only one kind of weak supervision in data. In…
cs.LG2019★ 1 cited
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
Lan-Zhe Guo, Yu-Feng Li, Ming Li +3
Weakly supervised data are widespread and have attracted much attention. However, since label quality is often difficult to guarantee, sometimes the use of weakly supervised data w…