  We consider the problem of domain generalization, namely, how to learn
representations given data from a set of domains that generalize to data from a
previously unseen domain. We propose the Domain Invariant Variational
Autoencoder (DIVA), a generative model that tackles this problem by learning
three independent latent subspaces, one for the domain, one for the class, and
one for any residual variations. We highlight that due to the generative nature
of our model we can also incorporate unlabeled data from known or previously
unseen domains. To the best of our knowledge this has not been done before in a
domain generalization setting. This property is highly desirable in fields like
medical imaging where labeled data is scarce. We experimentally evaluate our
model on the rotated MNIST benchmark and a malaria cell images dataset where we
show that (i) the learned subspaces are indeed complementary to each other,
(ii) we improve upon recent works on this task and (iii) incorporating
unlabelled data can boost the performance even further.
