  Unsupervised domain adaptation is used in many machine learning applications
where, during training, a model has access to unlabeled data in the target
domain, and a related labeled dataset. In this paper, we introduce a novel and
general domain-adversarial framework. Specifically, we derive a novel
generalization bound for domain adaptation that exploits a new measure of
discrepancy between distributions based on a variational characterization of
f-divergences. It recovers the theoretical results from Ben-David et al.
(2010a) as a special case and supports divergences used in practice. Based on
this bound, we derive a new algorithmic framework that introduces a key
correction in the original adversarial training method of Ganin et al. (2016).
We show that many regularizers and ad-hoc objectives introduced over the last
years in this framework are then not required to achieve performance comparable
to (if not better than) state-of-the-art domain-adversarial methods.
Experimental analysis conducted on real-world natural language and computer
vision datasets show that our framework outperforms existing baselines, and
obtains the best results for f-divergences that were not considered previously
in domain-adversarial learning.
