Jintao Huang

Jintao Huang, Yiu-ming Cheung, Chi-man Vong et al.

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-labeling from inter-class features, often overlooking the significant impact of the intra-class imbalanced features combined with the inter-class. To address these limitations, we introduce Granular Ball Representation for Imbalanced PLL (GBRIP), a novel framework for imbalanced PLL. GBRIP utilizes coarse-grained granular ball representation and multi-center loss to construct a granular ball-based nfeature space through unsupervised learning, effectively capturing the feature distribution within each class. GBRIP mitigates the impact of confusing features by systematically refining label disambiguation and estimating imbalance distributions. The novel multi-center loss function enhances learning by emphasizing the relationships between samples and their respective centers within the granular balls. Extensive experiments on standard benchmarks demonstrate that GBRIP outperforms existing state-of-the-art methods, offering a robust solution to the challenges of imbalanced PLL.

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 where Out-of-Distribution (OOD) objects are present. We introduce the OODPLL paradigm to tackle this significant yet underexplored issue. And our newly proposed PLOOD framework enables PLL to tackle OOD objects through Positive-Negative Sample Augmented (PNSA) feature learning and Partial Energy (PE)-based label refinement. The PNSA module enhances feature discrimination and OOD recognition by simulating in- and out-of-distribution instances, which employ structured positive and negative sample augmentation, in contrast to conventional PLL methods struggling to distinguish OOD samples. The PE scoring mechanism combines label confidence with energy-based uncertainty estimation, thereby reducing the impact of imprecise supervision and effectively achieving label disambiguation. Experimental results on CIFAR-10 and CIFAR-100, alongside various OOD datasets, demonstrate that conventional PLL methods exhibit substantial degradation in OOD scenarios, underscoring the necessity of incorporating OOD considerations in PLL approaches. Ablation studies show that PNSA feature learning and PE-based label refinement are necessary for PLOOD to work, offering a robust solution for open-set PLL problems.