Feature-Level Domain Adaptation

Domain adaptation is the supervised learning setting in which the training and test data originate from different domains: the so-called source and target domains. In this paper, we propose and study a domain adaption approach, called feature-level domain adaptation (flda), that models the dependence between two domains by means of a feature-level transfer distribution. The domain adapted classifier is trained by minimizing the expected loss under this transfer distribution. Our empirical evaluation of flda focuses on problems with binary and count features in which the domain adaptation can be naturally modeled via a dropout distribution, which allows the final classifier to adapt to the importance of specific features in the target data. Our experimental evaluation suggests that under certain conditions, flda converges to the classifier trained on the target distribution. Experiments with our domain adaptation approach on several real-world problems show that flda performs on par with state-of-the-art techniques in domain adaptation.