针对多标签持续学习中的类别不平衡问题，提出优化宏AUC的新方法。
Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
- 设计重加权损失函数与记忆更新策略，缓解标签不平衡影响。
- 在多个数据集上显著提升宏AUC指标，优于现有基线方法。
- 首次为多标签持续学习提供宏AUC的理论泛化分析，适合相关研究者参考。
在持续学习（CL）中，尽管现有工作主要聚焦于多分类任务，但对多标签学习（MLL）的研究仍较少。实际中，多标签数据集常存在类别不平衡，这在持续学习中尤为严峻。由于对不平衡敏感，宏AUC是多标签学习中常用且合适的评估指标。然而，目前尚无专门针对宏AUC优化的多标签持续学习研究。为此，本文提出一种基于记忆回放的方法，解决面向宏AUC的多标签持续学习中的不平衡问题。具体地，受近期理论工作的启发，提出新的重加权标签分布感知边界（RLDAM）损失；为适配该损失，设计新的记忆更新策略——权重保留更新（WRU），以在记忆中保持原始数据集中正负样本数量。理论上，我们分别在批量多标签学习和多标签持续学习设置下，提供了基于RLDAM算法的更优泛化分析。这是目前已知首个在多标签持续学习中提供宏AUC理论泛化分析的工作。实验结果表明，所提方法在多个基准上均优于现有基线。代码已开源：https://github.com/ML-Group-SDU/Macro-AUC-CL。
原文摘要 · Abstract (English)
In Continual Learning (CL), while existing work primarily focuses on the multi-class classification task, there has been limited research on Multi-Label Learning (MLL). In practice, MLL datasets are often class-imbalanced, making it inherently challenging, a problem that is even more acute in CL. Due to its sensitivity to imbalance, Macro-AUC is an appropriate and widely used measure in MLL. However, there is no research to optimize Macro-AUC in MLCL specifically. To fill this gap, in this paper, we propose a new memory replay-based method to tackle the imbalance issue for Macro-AUC-oriented MLCL. Specifically, inspired by recent theory work, we propose a new Reweighted Label-Distribution-Aware Margin (RLDAM) loss. Furthermore, to be compatible with the RLDAM loss, a new memory-updating strategy named Weight Retain Updating (WRU) is proposed to maintain the numbers of positive and negative instances of the original dataset in memory. Theoretically, we provide superior generalization analyses of the RLDAM-based algorithm in terms of Macro-AUC, separately in batch MLL and MLCL settings. This is the first work to offer theoretical generalization analyses in MLCL to our knowledge. Finally, a series of experimental results illustrate the effectiveness of our method over several baselines. Our codes are available at https://github.com/ML-Group-SDU/Macro-AUC-CL.
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