ParaUDA: Invariant Feature Learning With Auxiliary Synthetic Samples for Unsupervised Domain Adaptation
文献类型：期刊论文
| 作者 | Zhang, Wenwen5; Wang, Jiangong3,4 ; Wang, Yutong3,4; Wang, Fei-Yue1,2,3,4
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| 出版日期 | 2022-11-01 |
| 卷号 | 23期号:11页码:20217-20229 |
| ISSN号 | 1524-9050 |
| 关键词 | Adaptation models Representation learning Feature extraction Task analysis Semantics Generative adversarial networks Object detection Object detection unsupervised domain adaptation distribution alignment domain-invariant representation |
| DOI | 10.1109/TITS.2022.3176397 |
| 通讯作者 | Wang, Fei-Yue(feiyue.wang@ia.ac.cn) |
| 英文摘要 | Recognizing and locating objects by algorithms are essential and challenging issues for Intelligent Transportation Systems. However, the increasing demand for much labeled data hinders the further application of deep learning-based object detection. One of the optimal solutions is to train the target model with an existing dataset and then adapt it to new scenes, namely Unsupervised Domain Adaptation (UDA). However, most of existing methods at the pixel level mainly focus on adapting the model from source domain to target domain and ignore the essence of UDA to learn domain-invariant feature learning. Meanwhile, almost all methods at the feature level ignore to make conditional distributions matched for UDA while conducting feature alignment between source and target domain. Considering these problems, this paper proposes the ParaUDA, a novel framework of learning invariant representations for UDA in two aspects: pixel level and feature level. At the pixel level, we adopt CycleGAN to conduct domain transfer and convert the problem of original unsupervised domain adaptation to supervised domain adaptation. At the feature level, we adopt an adversarial adaption model to learn domain-invariant representation by aligning the distributions of domains between different image pairs with same mixture distributions. We evaluate our proposed framework in different scenes, from synthetic scenes to real scenes, from normal weather to challenging weather, and from scenes across cameras. The results of all the above experiments show that ParaUDA is effective and robust for adapting object detection models from source scenes to target scenes. |
| WOS关键词 | OBJECT DETECTION ; ALIGNMENT ; VISION |
| 资助项目 | Key-Area Research and Development Program of Guangdong Province[2020B090921003] ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; National Natural Science Foundation of China[U1811463] ; Key Research and Development Program 2020 of Guangzhou[202007050002] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000880752900025 |
| 资助机构 | Key-Area Research and Development Program of Guangdong Province ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; National Natural Science Foundation of China ; Key Research and Development Program 2020 of Guangzhou |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51296]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Wang, Fei-Yue |
| 作者单位 | 1.Qingdao Acad Intelligent Ind, Qingdao 266000, Peoples R China 2.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 5.Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Wenwen,Wang, Jiangong,Wang, Yutong,et al. ParaUDA: Invariant Feature Learning With Auxiliary Synthetic Samples for Unsupervised Domain Adaptation[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022,23(11):20217-20229. |
| APA | Zhang, Wenwen,Wang, Jiangong,Wang, Yutong,&Wang, Fei-Yue.(2022).ParaUDA: Invariant Feature Learning With Auxiliary Synthetic Samples for Unsupervised Domain Adaptation.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,23(11),20217-20229. |
| MLA | Zhang, Wenwen,et al."ParaUDA: Invariant Feature Learning With Auxiliary Synthetic Samples for Unsupervised Domain Adaptation".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 23.11(2022):20217-20229. |
入库方式： OAI收割
来源：自动化研究所
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