Dynamic-graph-based Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to learn an accurate classifier for a target domain by leveraging knowledge learned from a related (source) domain. Existing approaches focus on deriving new domain-invariant feature representations to align two domains and an extra classifier is required. In this paper, we propose a novel unsupervised domain adaptation method to train a classifier directly for ...