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 the target domain without learning the domain-invariant feature representation. For our method, the pseudo labels are assigned to target samples. An effective method is proposed to measure the relationship among cross-domain samples more accurately, so that we can construct a $p$ -nearest neighbor graph. Then label propagation is employed to update the target sample labels. The graph model and labels of target samples are expected to be updated alternately within an iterative framework. To further improve the classifier, a fuzzy classification and pseudo-label selection mechanism are utilized. Extensive experiments validate that our proposed method is superior or comparable to the state-of-the-art unsupervised domain adaptation methods.