用高斯桥增强长尾半监督学习的泛化能力
Geometric Regularization for Long-Tailed Semi-Supervised Learning via Gaussian Feature Bridges
- 构建类条件高斯特征桥，平滑模型从不确定到可靠原型的路径
- 在CIFAR10-LT和ImageNet-LT上显著提升长尾类别准确率
- 适合关注长尾分布与伪标签噪声问题的研究者
现实世界中的半监督学习常面临长尾标签分布和噪声伪标签的挑战，导致模型泛化能力下降并加剧确认偏差。本文提出新框架Gaussian Bridge Consistency (GBC)，通过在未标记样本与高质量类别原型之间构建语义插值路径来应对这些问题。该方法维护一个动态原型图谱，存储每类的多样化且持续更新的标注与伪标注样本。对每个未标记实例，GBC在潜在空间中构建类条件高斯特征桥，使学生模型能沿平滑轨迹从不确定预测走向可靠类别原型。沿此路径施加桥一致性损失，强制对几何插值目标分布的对齐。此外，提出BridgeMix策略，一种置信度感知的特征混合方法，通过同时插值样本与原型对，增强跨样本泛化能力。在CIFAR10-LT和ImageNet-LT（USB基准）上的大量实验验证了GBC在真实长尾半监督场景下的鲁棒性与有效性，持续提升长尾类别性能，且不牺牲可扩展性。
原文摘要 · Abstract (English)
Real-world semi-supervised learning (SSL) often encounters significant challenges with long-tailed label distributions and noisy pseudo-labels, which hinder generalization and amplify confirmation bias. In this work, we introduce a novel framework, Gaussian Bridge Consistency (GBC), to address these challenges by constructing semantic interpolation paths between unlabeled samples and high-quality class anchors. Our method maintains a dynamic Prototype Atlas that stores a diverse and evolving set of labeled and pseudo-labeled exemplars per class. For each unlabeled instance, GBC forms a class-conditional Gaussian Feature Bridge in the latent space, enabling the student model to traverse a smooth trajectory from uncertain predictions to reliable class prototypes. A bridge consistency loss is applied along this path to enforce alignment with a geometrically interpolated target distribution. Furthermore, we propose BridgeMix, a confidence-aware feature mixing strategy that interpolates both sample and anchor pairs to amplify cross-sample generalization. Extensive experiments on CIFAR10-LT and ImageNet-LT (USB benchmarks) validate the robustness and effectiveness of GBC under realistic long-tailed SSL settings, consistently improving long tail-class performance without sacrificing scalability.
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