Domain adaptation, as a task of reducing the annotation cost in a target domain by exploiting the existing labeled data in an auxiliary source domain, has received a lot of attention in the research community. However, the standard domain adaptation has assumed perfectly observed data in both domains, while in real world applications the existence of missing data can be prevalent. In this paper, we tackle a more challenging domain adaptation scenario where one has an incomplete target domain with partially observed data. We propose an Incomplete Data Imputation based Adversarial Network (IDIAN) model to address this new domain adaptation challenge. In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption. We conduct experiments on both cross-domain benchmark tasks and a real world adaptation task with imperfect target domains. The experimental results demonstrate the effectiveness of the proposed method.

翻译：域自适应作为一项通过利用辅助源域中已有的标注数据来降低目标域标注成本的任务，近年来在学术界受到广泛关注。然而，标准的域自适应方法假设两个域中的数据均被完整观测，而在现实应用中，数据缺失的情况普遍存在。本文针对一个更具挑战性的域自适应场景——目标域数据部分缺失（即不完整目标域）——展开研究。我们提出了一种基于不完整数据插补的对抗网络（Incomplete Data Imputation based Adversarial Network, IDIAN）模型来解决这一新挑战。在该模型中，我们设计了一个数据插补模块，基于目标域的部分观测值填补缺失特征，同时通过深度对抗自适应对齐两个域的分布。我们在跨域基准测试任务和一个真实世界的不完美目标域自适应任务上进行了实验，结果证明了所提方法的有效性。