  Unsupervised domain adaptation (uDA) models focus on pairwise adaptation
settings where there is a single, labeled, source and a single target domain.
However, in many real-world settings one seeks to adapt to multiple, but
somewhat similar, target domains. Applying pairwise adaptation approaches to
this setting may be suboptimal, as they fail to leverage shared information
among multiple domains. In this work we propose an information theoretic
approach for domain adaptation in the novel context of multiple target domains
with unlabeled instances and one source domain with labeled instances. Our
model aims to find a shared latent space common to all domains, while
simultaneously accounting for the remaining private, domain-specific factors.
Disentanglement of shared and private information is accomplished using a
unified information-theoretic approach, which also serves to establish a
stronger link between the latent representations and the observed data. The
resulting model, accompanied by an efficient optimization algorithm, allows
simultaneous adaptation from a single source to multiple target domains. We
test our approach on three challenging publicly-available datasets, showing
that it outperforms several popular domain adaptation methods.
