Wasserstein Uncertainty Estimation for Adversarial Domain Matching - Wang, Rui; Zhang, Ruiyi; Henao, Ricardo (Frontiers in Big Data, 2022)
Domain adaptation aims at reducing the domain shift between a labeled source domain and an unlabeled target domain, so that the source model can be generalized to target domains without fine tuning. In this paper, we propose to evaluate the cross-domain transferability between source and target samples by domain prediction uncertainty, which is quantified via Wasserstein gradient flows. Further, we exploit it for reweighting the training samples to alleviate the issue of domain shift. The proposed mechanism provides a meaningful curriculum for cross-domain transfer and rules out samples that are noisy or contain too much domain specific information during domain adaptation. Experiments on several benchmark datasets demonstrate that our approach can achieve state-of-the-art results in both balanced and partial domain adaptation.