解决多标签增量学习中标签缺失和类别不平衡问题
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
- 通过生成伪标签补充缺失标签信息
- 采用自适应加权分类器缓解多数类偏差
- 无需存储历史样本，适合持续学习场景
类增量学习（CIL）使模型能持续学习新类别而不遗忘旧知识。多标签类增量学习（MLCIL）将这一能力拓展到每个样本可能属于多个类别的现实场景，带来两大挑战：标签缺失导致历史信息不完整，以及类别不平衡引发模型偏向多数类。为此，我们提出无示例的标签增强分析适配方法（L3A）。L3A包含两个核心模块：伪标签（PL）模块通过为当前阶段样本生成伪标签来解决标签缺失问题；加权分析分类器（WAC）为神经网络提供闭式解，引入样本级权重以自适应调节类别贡献，缓解类别不平衡。在MS-COCO和PASCAL VOC数据集上的实验表明，L3A在MLCIL任务中优于现有方法。代码已公开于https://github.com/scut-zx/L3A。
原文摘要 · Abstract (English)
Class-incremental learning (CIL) enables models to learn new classes continually without forgetting previously acquired knowledge. Multi-label CIL (MLCIL) extends CIL to a real-world scenario where each sample may belong to multiple classes, introducing several challenges: label absence, which leads to incomplete historical information due to missing labels, and class imbalance, which results in the model bias toward majority classes. To address these challenges, we propose Label-Augmented Analytic Adaptation (L3A), an exemplar-free approach without storing past samples. L3A integrates two key modules. The pseudo-label (PL) module implements label augmentation by generating pseudo-labels for current phase samples, addressing the label absence problem. The weighted analytic classifier (WAC) derives a closed-form solution for neural networks. It introduces sample-specific weights to adaptively balance the class contribution and mitigate class imbalance. Experiments on MS-COCO and PASCAL VOC datasets demonstrate that L3A outperforms existing methods in MLCIL tasks. Our code is available at https://github.com/scut-zx/L3A.
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