A General Domain Specific Feature Transfer Framework for Hybrid Domain Adaptation

Heterogeneous domain adaptation needs supplementary information to link up different domains. However, such supplementary information may not always be available in real cases. In this paper, a new problem setting called hybrid domain adaptation is investigated. It is a special case of heterogeneous domain adaptation, in which different domains share some common features, but also have their own domain specific features. We leverage upon common features instead of supplementary information to achieve effective adaptation. We propose a general domain specific feature transfer framework, which can link up different domains using common features and simultaneously reduce domain divergences. Specifically, we learn the translations between common features and domain specific features. Then, we cross-use the learned translations to transfer the domain specific features of one domain to another domain. Finally, we compose a homogeneous space in which the domain divergences are minimized. We instantiate the general framework to a linear case and a nonlinear case. Extensive experiments verify the effectiveness of the two cases.