Source data‐free domain adaptation of object detector through domain‐specific perturbation

International Journal of Intelligent SystemsVolume 36, Issue 8 p. 3746-3766 RESEARCH ARTICLEToken Access Source data-free domain adaptation of object detector through domain-specific perturbation Lin Xiong, orcid.org/0000-0002-8303-3216 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of ChinaSearch for more papers by this authorMao Ye, Corresponding Author cvlab.uestc@gmail.com orcid.org/0000-0003-4760-8702 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of China Correspondence Mao Ye, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Western High-Tech Industrial Zone, Chengdu 611731, P.R. China. Email: cvlab.uestc@gmail.comSearch for more papers by this authorDan Zhang, orcid.org/0000-0001-7429-3609 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of ChinaSearch for more papers by this authorYan Gan, orcid.org/0000-0002-6716-0039 College of Computer Science, Chongqing University, Chongqing, People's Republic of ChinaSearch for more papers by this authorXue Li, orcid.org/0000-0002-4515-6792 School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Queensland, AustraliaSearch for more papers by this authorYingying Zhu, orcid.org/0000-0003-3920-5890 College of Engineering, University of Texas Arlington, Arlington, Texas, USASearch for more papers by this author Lin Xiong, orcid.org/0000-0002-8303-3216 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of ChinaSearch for more papers by this authorMao Ye, Corresponding Author cvlab.uestc@gmail.com orcid.org/0000-0003-4760-8702 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of China Correspondence Mao Ye, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Western High-Tech Industrial Zone, Chengdu 611731, P.R. China. Email: cvlab.uestc@gmail.comSearch for more papers by this authorDan Zhang, orcid.org/0000-0001-7429-3609 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of ChinaSearch for more papers by this authorYan Gan, orcid.org/0000-0002-6716-0039 College of Computer Science, Chongqing University, Chongqing, People's Republic of ChinaSearch for more papers by this authorXue Li, orcid.org/0000-0002-4515-6792 School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Queensland, AustraliaSearch for more papers by this authorYingying Zhu, orcid.org/0000-0003-3920-5890 College of Engineering, University of Texas Arlington, Arlington, Texas, USASearch for more papers by this author First published: 08 May 2021 https://doi.org/10.1002/int.22434Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract The current unsupervised cross-domain detection methods need source domain data to retrain the detection model in target domain. However, the source domain data may be unavailable due to privacy, decentralization, or computation resource restrictions. A natural idea is to optimize the parameters of the source domain model by self-supervised learning based on pseudo labels. We propose another approach from the viewpoint of noise perturbation without pseudo-labeling. It can be assumed that the source and target domains are actually derived from a domain invariant space through domain-specific perturbations, respectively. A super target domain can be constructed by augmenting more target domain perturbations to the target domain images. The optimal direction of the target domain to the domain invariant space can be approximated as the alignment direction from the super target domain to the target domain. Based on this idea, we propose a novel method called SOAP (SOurce data-free domain Adaptation through domain Perturbation) which can remove domain perturbation from the target domain. The image-level, instance-level, and category consistency regularizations based on Mean Teacher structure are proposed to learn the correct alignment direction. Specifically, the category consistency can also further improve the classification accuracy. Extensive experiments on multiple domain adaptation scenarios demonstrate that SOAP achieves better performance surpassing the baseline (Faster R-CNN) and multiple state-of-the-art domain adaptation methods which need to access source domain data. Volume36, Issue8August 2021Pages 3746-3766 RelatedInformation