The goal of the paper is to design active learning strategies which lead to domain adaptation under an assumption of domain shift in the case of Lipschitz labeling function. Building on previous work by Mansour et al. (2009) we adapt the concept of discrepancy distance between source and target distributions to restrict the maximization over the hypothesis class to a localized class of functions which are performing accurate labeling on the source domain. We derive generalization error bounds for such active learning strategies in terms of Rademacher average and localized discrepancy for general loss functions which satisfy a regularity condition. Practical algorithms are inferred from the theoretical bounds, one is based on greedy optimization and the other is a K-medoids algorithm. We also provide improved versions of the algorithms to address the case of large data sets. These algorithms are competitive against other state-of-the-art active learning techniques in the context of domain adaptation as shown in our numerical experiments, in particular on large data sets of around one hundred thousand images.

论文旨在设计主动学习策略，实现基于Lipschitz函数假设的领域自适应。通过对Mansour等人（2009）的先前工作进行补充，我们将源和目标分布之间的差异距离概念适应于假设类的最大化，来局限于在源域上执行准确标记的函数类。我们针对满足正则条件的一般损失函数导出了这种主动学习策略的推广误差边界，用于兰德马赫平均值和局部差异度量。从理论边界中推导出实用的K-medoids算法，可以处理大数据集的情况。我们的数值实验表明，在领域自适应的情况下，特别是在约十万个图像的大数据集上，所提出的算法与其他最先进的主动学习技术具有竞争力。

基于差异性的域自适应主动学习