An essential problem in domain adaptation is to understand and make use of
distribution changes across domains. For this purpose, we first propose a
flexible Generative Domain Adaptation Network (G-DAN) with specific latent
variables to capture changes in the generating process of features across
domains. By explicitly modeling the changes, one can even generate data in new
domains using the generating process with new values for the latent variables
in G-DAN. In practice, the process to generate all features together may
involve high-dimensional latent variables, requiring dealing with distributions
in high dimensions and making it difficult to learn domain changes from few
source domains. Interestingly, by further making use of the causal
representation of joint distributions, we then decompose the joint distribution
into separate modules, each of which involves different low-dimensional latent
variables and can be learned separately, leading to a Causal G-DAN (CG-DAN).
This improves both statistical and computational efficiency of the learning
procedure. Finally, by matching the feature distribution in the target domain,
we can recover the target-domain joint distribution and derive the learning
machine for the target domain. We demonstrate the efficacy of both G-DAN and
CG-DAN in domain generation and cross-domain prediction 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。