  Domain adaptation aims to leverage the supervision signal of source domain to
obtain an accurate model for target domain, where the labels are not available.
To leverage and adapt the label information from source domain, most existing
methods employ a feature extracting function and match the marginal
distributions of source and target domains in a shared feature space. In this
paper, from the perspective of information theory, we show that representation
matching is actually an insufficient constraint on the feature space for
obtaining a model with good generalization performance in target domain. We
then propose variational bottleneck domain adaptation (VBDA), a new domain
adaptation method which improves feature transferability by explicitly
enforcing the feature extractor to ignore the task-irrelevant factors and focus
on the information that is essential to the task of interest for both source
and target domains. Extensive experimental results demonstrate that VBDA
significantly outperforms state-of-the-art methods across three domain
adaptation benchmark datasets.
