  Unsupervised domain adaptation aims at learning a shared model for two
related, but not identical, domains by leveraging supervision from a source
domain to an unsupervised target domain. A number of effective domain
adaptation approaches rely on the ability to extract discriminative, yet
domain-invariant, latent factors which are common to both domains. Extracting
latent commonality is also useful for disentanglement analysis, enabling
separation between the common and the domain-specific features of both domains.
In this paper, we present a method for boosting domain adaptation performance
by leveraging disentanglement analysis. The key idea is that by learning to
separately extract both the common and the domain-specific features, one can
synthesize more target domain data with supervision, thereby boosting the
domain adaptation performance. Better common feature extraction, in turn, helps
further improve the disentanglement analysis and disentangled synthesis. We
show that iterating between domain adaptation and disentanglement analysis can
consistently improve each other on several unsupervised domain adaptation
tasks, for various domain adaptation backbone models.
