  A dominant approach for addressing unsupervised domain adaptation is to map
data points for the source and the target domains into an embedding space which
is modeled as the output-space of a shared deep encoder. The encoder is trained
to make the embedding space domain-agnostic to make a source-trained classifier
generalizable on the target domain. A secondary mechanism to improve UDA
performance further is to make the source domain distribution more compact to
improve model generalizability. We demonstrate that increasing the interclass
margins in the embedding space can help to develop a UDA algorithm with
improved performance. We estimate the internally learned multi-modal
distribution for the source domain, learned as a result of pretraining, and use
it to increase the interclass class separation in the source domain to reduce
the effect of domain shift. We demonstrate that using our approach leads to
improved model generalizability on four standard benchmark UDA image
classification datasets and compares favorably against exiting methods.
