提出新型粒度球表示法，解决类别不平衡下的部分标签学习难题。
GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning
- 用粒度球结构建模类内特征分布，捕捉类别内部差异。
- 多中心损失提升样本与类心关联，改善标签歧义问题。
- 在标准数据集上超越现有方法，适合处理不平衡部分标签任务。
部分标签学习（PLL）是一种复杂的弱监督多分类任务，受类别不平衡影响显著。现有方法仅依赖类间特征进行伪标签生成，常忽视类内不平衡特征与类间关系的共同作用。为此，本文提出针对不平衡部分标签学习的粒度球表示框架（GBRIP）。该框架通过无监督学习构建基于粒度球的特征空间，利用粗粒度粒度球表示和多中心损失函数，有效捕捉各类别内部的特征分布。GBRIP通过系统性优化标签消歧并估计不平衡分布，缓解混淆特征的影响。多中心损失函数强化了样本与其所属粒度球中心的关系学习。大量实验表明，GBRIP在标准基准上优于现有最先进方法，为不平衡部分标签学习提供了稳健解决方案。
原文摘要 · Abstract (English)
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.
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