AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows

Understand

Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain.

• Variants of this problem have been studied in many contexts, such as cross-domain translation and domain adaptation.
• We propose AlignFlow, a generative modeling framework that models each domain via a normalizing flow.
• The use of normalizing flows allows for a) flexibility in specifying learning objectives via adversarial training, maximum likelihood estimation, or a hybrid of the two methods; and b) learning and exact inference of a shared representation in the latent space of the generative model.