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Domain-Aware Graph Network for Bridging Multi-Source Domain Adaptation

文献类型：期刊论文

作者Yuan, Jin3,4; Hou, Feng2,5; Yang, Ying1; Zhang, Yang1; Shi, Zhongchao1; Geng, Xin3,4; Fan, Jianping1; He, Zhiqiang2,5; Rui, Yong1,3,4
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2024
卷号26页码:7210-7224
关键词Task analysis Feature extraction Graph neural networks Adaptation models Self-supervised learning Multitasking Image color analysis Multi-source domain adaptation self-supervised learning graph neural network real-world applications
ISSN号1520-9210
DOI10.1109/TMM.2024.3361729
英文摘要Domain adaptation (DA) addresses the challenge of distribution discrepancy between the training and test data, while multi-source domain adaptation (MSDA) is particularly appealing for realistic scenarios. With the emergence of extensive unlabeled datasets, self-supervised learning has gained significant popularity in deep learning. It is noteworthy that multi-source domain adaptation and self-supervised learning share a common objective: leveraging unlabeled data to acquire more informative representations. However, conventional self-supervised learning encounters two main limitations. Firstly, the traditional pretext task falls to transfer fine-grained knowledge to downstream task with general representation learning. Secondly, the scheme of the same feature extractor with distinct prediction heads makes the cross-task knowledge exchange and information sharing ineffective. In order to tackle these challenges, we introduce a novel approach called Domain-Aware Graph Network (DAGNet). DAGNet utilizes a graph neural network as a bridge to facilitate efficient cross-task knowledge exchange. By employing a mask token strategy, we enhance the robustness of representations by selectively masking certain domain or self-supervised information. In terms of datasets, the uneven and style-based domain shifts in current datasets make it challenging to measure the model's domain adaptation performance in real-world applications. To address this issue, we introduce a benchmark dataset DomainVerse with continuous spatio-temporal domain shifts encountered in the real world. Our extensive experiments demonstrate that DAGNet achieves state-of-the-art performance not only on mainstream multi-source domain adaptation datasets but also on different settings within DomainVerse.
资助项目AI Lab
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:001209811000018
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/38990]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Geng, Xin; Rui, Yong
作者单位1.Lenovo Res, AI Lab, Beijing 100000, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100000, Peoples R China
3.Southeast Univ, Sch Comp Sci & Engn, Minist Educ, Nanjing 211189, Peoples R China
4.Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 211189, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Beijing 100000, Peoples R China
推荐引用方式
GB/T 7714		 Yuan, Jin,Hou, Feng,Yang, Ying,et al. Domain-Aware Graph Network for Bridging Multi-Source Domain Adaptation[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2024,26:7210-7224.
APA Yuan, Jin.,Hou, Feng.,Yang, Ying.,Zhang, Yang.,Shi, Zhongchao.,...&Rui, Yong.(2024).Domain-Aware Graph Network for Bridging Multi-Source Domain Adaptation.IEEE TRANSACTIONS ON MULTIMEDIA,26,7210-7224.
MLA Yuan, Jin,et al."Domain-Aware Graph Network for Bridging Multi-Source Domain Adaptation".IEEE TRANSACTIONS ON MULTIMEDIA 26(2024):7210-7224.

入库方式： OAI收割

来源：计算技术研究所

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