4 papers
Exploiting the Potential Supervision Information of Clean Samples in Partial Label Learning
Guangtai Wang, Chi-Man Vong, Jintao Huang
Diminishing the impact of false-positive labels is critical for conducting disambiguation in partial label learning. However, the existing disambiguation strategies mainly focus on…
GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning
Jintao Huang, Yiu-ming Cheung, Chi-man Vong +1
Partial label learning (PLL) is a complicated weakly supervised multi-classification task compounded by class imbalance. Currently, existing methods only rely on inter-class pseudo…
PLOOD: Partial Label Learning with Out-of-distribution Objects
Jintao Huang, Yiu-Ming Cheung, Chi-Man Vong
Existing Partial Label Learning (PLL) methods posit that training and test data adhere to the same distribution, a premise that frequently does not hold in practical application wh…
Graph based Label Enhancement for Multi-instance Multi-label learning
Houcheng Su, Jintao Huang, Daixian Liu +3
Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with mu…