Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain. Whereas, in the real-world scenario's it might be feasible to get labels for a small proportion of target data. In these scenarios, it is important to select maximally-informative samples to label and find an effective way to combine them with the existing knowledge from source data. Towards achieving this, we propose S$^3$VAADA which i) introduces a novel submodular criterion to select a maximally informative subset to label and ii) enhances a cluster-based DA procedure through novel improvements to effectively utilize all the available data for improving generalization on target. Our approach consistently outperforms the competing state-of-the-art approaches on datasets with varying degrees of domain shifts.

本文提出了一种新的无监督领域自适应方法S^3VAADA，通过引入新颖的子模块准则来选择最大的信息子集来标记，增强了基于簇的DA过程，并通过新的改进有效地利用所有可用的数据来提高目标领域问题的泛化性能。在具有不同程度的领域转移的数据集上，我们的方法始终优于竞争的最先进方法。

S$^3$VAADA: 虚拟对抗主动领域自适应的次模子集选择