兼顾不确定与确定样本，提升域适应标注效率
Certainty and Uncertainty Guided Active Domain Adaptation
- 先标记不确定样本，再将高置信预测当作真值
- 在Office-Home和DomainNet上准确率显著超越现有方法
- 适合需要减少人工标注成本的迁移学习场景
主动域适应（ADA）通过选择性标注少量目标域样本实现模型适配。现有方法仅关注不确定性高的样本，忽视了那些预测置信度高且常接近真实标签的样本。我们发现，在主动采样前将高置信预测纳入已标注集，可缩小搜索空间并提升适配效果。为此，提出一种协同框架：结合基于高斯过程的主动采样（GPAS）识别不确定样本，以及基于伪标签的确定性采样（PLCS）处理高置信预测，逐步增强适配性能。PLCS优化搜索空间，GPAS缩小域间差异，提高高置信样本比例。在Office-Home和DomainNet上的大量实验表明，该方法优于当前最优的ADA方法。
原文摘要 · Abstract (English)
Active Domain Adaptation (ADA) adapts models to target domains by selectively labeling a few target samples. Existing ADA methods prioritize uncertain samples but overlook confident ones, which often match ground-truth. We find that incorporating confident predictions into the labeled set before active sampling reduces the search space and improves adaptation. To address this, we propose a collaborative framework that labels uncertain samples while treating highly confident predictions as ground truth. Our method combines Gaussian Process-based Active Sampling (GPAS) for identifying uncertain samples and Pseudo-Label-based Certain Sampling (PLCS) for confident ones, progressively enhancing adaptation. PLCS refines the search space, and GPAS reduces the domain gap, boosting the proportion of confident samples. Extensive experiments on Office-Home and DomainNet show that our approach outperforms state-of-the-art ADA methods.
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