Duplex adversarial networks for multiple-source domain adaptation

摘要

Domain adaptation is a powerful tool for transferring the knowledge of the source domain with sufficient annotations for target tasks. However, most existing domain adaptation methods focus on the single-source–single-target adaptation setting, which ignore that existing different but related multiple source domains are available for target tasks. In this paper, we propose a novel theoretical generalization bound for multiple source domain adaptation, which averages the multiple source hypothesis risks by using HΔH-distance with an assumption that the target hypothesis is the combination of all the source hypotheses. Following this theoretical result, we propose a new algorithm called Duplex Adversarial Networks for Multi-source Adaptation (DAN_MA): one adversarial network is utilized for feature generator to learn domain invariant representations between source and target domains with a domain classifier for each pair of source and target; the other is utilized to eliminate the domain characteristics among different sources by making target samples close to each source task-specific decision boundaries. The shared feature generator aims to learn target representations which are invariant to the multiple domain shifts while still close to the source decision boundaries. Extensive experiments show that our method outperforms other domain adaptation methods on three public datasets.