用类别比例先验缓解数据不均衡下的半监督学习偏差
Leveraging Label Proportion Prior for Class-Imbalanced Semi-Supervised Learning
- 引入标签比例损失作为正则项，约束模型预测分布
- 在长尾CIFAR-10上，不同不平衡程度和标签率下均提升性能
- 适合标签稀缺且类别严重不均衡的场景
半监督学习在类别不均衡场景下表现不佳，伪标签会放大多数类偏差并压制少数类表现。本文提出轻量级框架，首次将来自标签比例学习（LLP）的比例损失引入半监督学习作为正则项。该损失使模型预测与全局类别分布对齐，有效缓解多数类与少数类间的偏差。为增强训练稳定性，进一步设计了考虑小批量组成波动的随机变体。在长尾CIFAR-10基准上的实验表明，将比例损失融入FixMatch和ReMixMatch，在不同不均衡程度和标签比例下均持续优于基线方法，且在标签稀缺条件下性能媲美或超越现有类别不均衡半监督学习方法。
原文摘要 · Abstract (English)
Semi-supervised learning (SSL) often suffers under class imbalance, where pseudo-labeling amplifies majority bias and suppresses minority performance. We address this issue with a lightweight framework that, to our knowledge, is the first to introduce Proportion Loss from learning from label proportions (LLP) into SSL as a regularization term. Proportion Loss aligns model predictions with the global class distribution, mitigating bias across both majority and minority classes. To further stabilize training, we formulate a stochastic variant that accounts for fluctuations in mini-batch composition. Experiments on the Long-tailed CIFAR-10 benchmark show that integrating Proportion Loss into FixMatch and ReMixMatch consistently improves performance over the baselines across imbalance severities and label ratios, and achieves competitive or superior results compared to existing CISSL methods, particularly under scarce-label conditions.
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