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：作为利用辅助源域现有标签数据减少目标领域注释成本的任务,标准领域适应工作在研究界受到了很多关注,但标准领域适应工作假定了这两个领域都完全观测到的数据,而在现实世界应用中,缺失数据的存在可能很普遍。在本文件中,我们处理的是一个更具挑战性的领域适应方案,即一个人有一个不完整的目标领域,有部分观测到的数据。我们提议了一个基于数据模拟的不完全的反向网络(DIIAN)模型,以应对这一新领域适应挑战。在拟议的模型中,我们设计了一个数据估算模块,以填补基于目标领域部分观察的数据缺失的特征值,同时通过深入的对立调整对立调整对两个领域进行调整。我们在跨主题基准任务和真实的世界适应任务方面进行实验,但目标领域不完善。实验结果显示了拟议方法的有效性。