Multi-source domain adaptation has attracted great attention in machine learning
community. Most of these methods focus on weighting the predictions produced by the …

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source
domains to the unlabeled target domain. In this paper, we propose a cycle self-refinement …

Early Unsupervised Domain Adaptation (UDA) methods have mostly assumed the setting of
a single source domain, where all the labeled source data come from the same distribution …

Abstract Multi-Source Domain Adaptation (MSDA) aims at transferring knowledge from
multiple labeled source domains to benefit the task in an unlabeled target domain. The …

Abstract Multi-source Domain Adaptation (MSDA) is more practical but challenging than the
conventional unsupervised domain adaptation due to the involvement of diverse multiple …

Recent works of multi-source domain adaptation focus on learning a domain-agnostic
model, of which the parameters are static. However, such a static model is difficult to handle …

Abstract The performance of Multi-Source Unsupervised Domain Adaptation depends
significantly on the effectiveness of transfer from labeled source domain samples. In this …

Unsupervised domain adaptation (UDA) makes predictions for the target domain data while
manual annotations are only available in the source domain. Previous methods minimize …

Abstract Domain adaptation is a powerful tool for transferring the knowledge of the source
domain with sufficient annotations for target tasks. However, most existing domain …

Domain adaptation, which transfers the knowledge from label-rich source domain to
unlabeled target domains, is a challenging task in machine learning. The prior domain …