面向 Macro-AUC 的不平衡多标签持续学习
Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
AI总结：
本文针对多标签持续学习（MLCL）中的类别不平衡问题，首次提出基于记忆重放的方法，结合重新加权的标签分布感知边界（RLDAM）损失和权重保留更新（WRU）策略，以优化Macro-AUC，并提供了首个理论泛化分析及实验验证。
摘要：
在持续学习（CL）中，尽管现有工作主要关注多类分类任务，但对多标签学习（MLL）的研究仍然有限。在实践中，MLL数据集通常存在类别不平衡问题，使其具有内在的挑战性，这一问题在CL中更为突出。由于对不平衡具有敏感性，Macro-AUC是MLL中一种合适且被广泛使用的度量标准。然而，目前尚无专门针对MLCL优化Macro-AUC的研究。为了填补这一空白，本文提出了一种新的基于记忆重放的方法，以解决面向Macro-AUC的MLCL中的不平衡问题。具体而言，受近期理论工作的启发，我们提出了一种新的重新加权的标签分布感知边界（RLDAM）损失。此外，为了与RLDAM损失兼容，提出了一种名为权重保留更新（WRU）的新记忆更新策略，以在记忆中保持原始数据集的正负实例数量。在理论上，我们分别在批次MLL和MLCL设置下，提供了基于RLDAM算法在Macro-AUC方面的优越泛化分析。据我们所知，这是首个在MLCL中提供理论泛化分析的工作。最后，一系列实验结果说明了我们的方法相对于几个基线的有效性。我们的代码可在 https://github.com/ML-Group-SDU/Macro-AUC-CL 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
