  We propose a method for unsupervised domain adaptation that trains a shared
embedding to align the joint distributions of inputs (domain) and outputs
(classes), making any classifier agnostic to the domain. Joint alignment
ensures that not only the marginal distributions of the domain are aligned, but
the labels as well. We propose a novel objective function that encourages the
class-conditional distributions to have disjoint support in feature space. We
further exploit adversarial regularization to improve the performance of the
classifier on the domain for which no annotated data is available.
