73 citations · 256 across the 36 of their papers we have counts for
Showing 2023 · cs.LGShow all
3 papers · 2 filters
cs.LG2023
No Regularization is Needed: An Efficient and Effective Model for Incomplete Label Distribution Learning
Xiang Li, Songcan Chen
Label Distribution Learning (LDL) assigns soft labels, a.k.a. degrees, to a sample. In reality, it is always laborious to obtain complete degrees, giving birth to the Incomplete LD…
cs.LG2023
Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning
Wenhai Wan, Xinrui Wang, Ming-Kun Xie +3
Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise. However, a more common scenario in the real world is the presence of both…
cs.LG2023
Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels
Xiang Li, Xinrui Wang, Songcan Chen
In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge,…