We propose a new generative model for domain adaptation, in which training data (source domain) and test data (target domain) come from different distributions. An essential problem in domain adaptation is to understand how the distribution shifts across domains. For this purpose, we propose a generative domain adaptation network to understand and identify the domain changes, which enables the generation of new domains. In addition, focusing on single domain adaptation, we demonstrate how our model recovers the joint distribution on the target domain from unlabeled target domain data by transferring valuable information between domains. Finally, to improve transfer efficiency, we build a causal generative domain adaptation network by decomposing the joint distribution of features and labels into a series of causal modules according to a causal model. Due to the modularity property of a causal model, we can improve the identification of distribution changes by modeling each causal modules separately. With the proposed adaptation networks, the predictive model on the target domain can be easily trained on data sampled from the learned networks. We demonstrate the efficacy of our method on both synthetic and real data experiments.

本文提出了一种灵活的生成领域自适应网络(G-DAN)和因果生成领域自适应网络(CG-DAN)，旨在通过建模特征的生成过程和使用低维潜在变量，学习和处理源域和目标域之间的分布变化，进而实现跨领域数据生成和预测。实验结果表明，这两种方法在合成和真实数据实验中都能有效地适应不同的领域。