CLC number: TP181
On-line Access: 2022-12-14
Received: 2022-06-29
Revision Accepted: 2022-12-17
Crosschecked: 2022-09-22
Cited: 0
Clicked: 1275
Citations: Bibtex RefMan EndNote GB/T7714
Yikang WEI, Yahong HAN. Dual collaboration for decentralized multi-source domain adaptation[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2200284 @article{title="Dual collaboration for decentralized multi-source domain adaptation", %0 Journal Article TY - JOUR
双向协同的去中心化多源域自适应1天津大学智能与计算学部,中国天津市,300350 2天津大学天津市机器学习重点实验室,中国天津市,300350 摘要:去中心化多源域自适应是指在数据去中心化场景下执行无监督多源域自适应。数据去中心化的挑战是源域与目标域在训练中缺乏跨域协同。对于无标签的目标域,目标域模型需要在源域模型的协助下迁移监督知识,而域差距会导致源域模型的适应性能有限。对于有标签的源域,源域模型在数据去中心化场景下倾向于过拟合本地数据,从而导致负迁移问题。对于以上挑战,提出双向协同的去中心化多源域自适应方法,通过其它域模型的协助进行局部源域模型与局部目标域模型的协同训练与聚合。对于目标域,我们在源域模型的协助下蒸馏监督知识,同时完全利用无标签目标域的数据来缓解域偏移问题。对于源域,我们在目标域模型的协助下正则化源域模型来避免负迁移问题。以上过程在去中心化的源域和目标域之间形成一种双向协同,以便在数据去中心化场景下提升域自适应性能。在标准多源域自适应数据集上的实验表明,我们的方法以较大优势优于现有的多源域自适应方法。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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