论文标题
打击活跃域适应的标签分布变化
Combating Label Distribution Shift for Active Domain Adaptation
论文作者
论文摘要
我们考虑了主动域适应性（ADA）对未标记的目标数据的问题，其中有预算限制的标签集被主动选择并标记为标签。受到对域适应性源和目标之间的标签分布不匹配的关键问题的最新分析的启发，我们设计了一种在ADA中首次解决该问题的方法。它的核心是一种新型的抽样策略，该策略寻求目标数据，以最能近似整个目标分布以及代表性，多样化和不确定。然后，采样目标数据不仅用于监督学习，还用于匹配源和目标域的标签分布，从而导致显着的性能提高。在四个公共基准测试中，我们的方法在每个适应方案中都大大优于现有方法。
We consider the problem of active domain adaptation (ADA) to unlabeled target data, of which subset is actively selected and labeled given a budget constraint. Inspired by recent analysis on a critical issue from label distribution mismatch between source and target in domain adaptation, we devise a method that addresses the issue for the first time in ADA. At its heart lies a novel sampling strategy, which seeks target data that best approximate the entire target distribution as well as being representative, diverse, and uncertain. The sampled target data are then used not only for supervised learning but also for matching label distributions of source and target domains, leading to remarkable performance improvement. On four public benchmarks, our method substantially outperforms existing methods in every adaptation scenario.
