Causal Generative Domain Adaptation Networks

Understand

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.