  Existing methods for unsupervised domain adaptation often rely on minimizing
some statistical distance between the source and target samples in the latent
space. To avoid the sampling variability, class imbalance, and data-privacy
concerns that often plague these methods, we instead provide a memory and
computation-efficient probabilistic framework to extract class prototypes and
align the target features with them. We demonstrate the general applicability
of our method on a wide range of scenarios, including single-source,
multi-source, class-imbalance, and source-private domain adaptation. Requiring
no additional model parameters and having a moderate increase in computation
over the source model alone, the proposed method achieves competitive
performance with state-of-the-art methods.
