DANE: A Dual-level Alignment Network with Ensemble Learning for Multi-Source Domain Adaptation
Multisource domain adaptation (MDA) aims to transfer knowledge from multiple labeled
source domains to an unlabeled target domain. However, the severe intradomain and …
source domains to an unlabeled target domain. However, the severe intradomain and …
Mutual learning of joint and separate domain alignments for multi-source domain adaptation
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 …
multiple labeled source domains to benefit the task in an unlabeled target domain. The …
Contrastive adaptation network for single-and multi-source domain adaptation
Unsupervised domain adaptation (UDA) makes predictions for the target domain data while
manual annotations are only available in the source domain. Previous methods minimize …
manual annotations are only available in the source domain. Previous methods minimize …
Robust target training for multi-source domain adaptation
Given multiple labeled source domains and a single target domain, most existing multi-
source domain adaptation (MSDA) models are trained on data from all domains jointly in …
source domain adaptation (MSDA) models are trained on data from all domains jointly in …
A new progressive multisource domain adaptation network with weighted decision fusion
Multisource unsupervised domain adaptation (MUDA) is an important and challenging topic
for target classification with the assistance of labeled data in source domains. When we …
for target classification with the assistance of labeled data in source domains. When we …
Deep joint semantic adaptation network for multi-source unsupervised domain adaptation
Abstract Multi-source Unsupervised Domain Adaptation (MUDA) transfers knowledge
learned from multiple labeled source domains to an unlabeled target domain by minimizing …
learned from multiple labeled source domains to an unlabeled target domain by minimizing …
T-svdnet: Exploring high-order prototypical correlations for multi-source domain adaptation
Most existing domain adaptation methods focus on adaptation from only one source domain,
however, in practice there are a number of relevant sources that could be leveraged to help …
however, in practice there are a number of relevant sources that could be leveraged to help …
Domain attention consistency for multi-source domain adaptation
Most existing multi-source domain adaptation (MSDA) methods minimize the distance
between multiple source-target domain pairs via feature distribution alignment, an approach …
between multiple source-target domain pairs via feature distribution alignment, an approach …
Cycle Self-Refinement for Multi-Source Domain Adaptation
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 …
domains to the unlabeled target domain. In this paper, we propose a cycle self-refinement …
Multi-source unsupervised domain adaptation via pseudo target domain
CX Ren, YH Liu, XW Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source
domains to an unlabeled target domain. MDA is a challenging task due to the severe …
domains to an unlabeled target domain. MDA is a challenging task due to the severe …