Unsupervised domain adaptation uses source data from different distributions to solve the problem of classifying data from unlabeled target domains. However, conventional methods require access to source data, which often raise concerns about data privacy. In this paper, we consider a more practical but challenging setting where the source domain data is unavailable and the target domain data is unlabeled. Specifically, we address the domain discrepancy problem from the perspective of contrastive learning. The key idea of our work is to learn a domain-invariant feature by 1) performing clustering directly in the original feature space with nearest neighbors; 2) constructing truly hard negative pairs by extended neighbors without introducing additional computational complexity; and 3) combining noise-contrastive estimation theory to gain computational advantage. We conduct careful ablation studies and extensive experiments on three common benchmarks: VisDA, Office-Home, and Office-31. The results demonstrate the superiority of our methods compared with other state-of-the-art works.

翻译：无监督域自适应利用来自不同分布的源数据解决未标注目标域数据的分类问题。然而，传统方法需访问源数据，这常引发数据隐私担忧。本文考虑一种更实际且具挑战性的场景：源域数据不可获取，目标域数据未标注。具体而言，我们从对比学习视角解决域差异问题。本工作的核心思想是通过以下方式学习域不变特征：1）在原始特征空间中直接基于最近邻进行聚类；2）利用扩展邻域构建真正困难的负样本对，且不增加额外计算复杂度；3）结合噪声对比估计理论获得计算优势。我们在三个通用基准数据集（VisDA、Office-Home、Office-31）上开展了细致的消融实验与广泛比较。结果表明，我们的方法相较于其他当前最优方法具有显著优势。