{"type":"ScholarlyArticle","title":"Independent Feature Decomposition and Instance Alignment for Unsupervised Domain Adaptation","authors":[{"name":"Qichen He","slug":"qichen-he","given":"Qichen","family":"He","affiliation":"School of CSE , University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Siying Xiao","slug":"siying-xiao","given":"Siying","family":"Xiao","affiliation":"School of CSE , University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Mao Ye","slug":"mao-ye","given":"Mao","family":"Ye","affiliation":"School of CSE , University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Xiatian Zhu","slug":"xiatian-zhu","given":"Xiatian","family":"Zhu","affiliation":"Surrey Institute for People-Centred Artificial Intelligence , CVSSP , University of Surrey , Guildford , UK"},{"name":"Ferrante Neri","slug":"ferrante-neri","given":"Ferrante","family":"Neri","affiliation":"NICE Research Group , Department of Computer Science , University of Surrey , Guildford , UK"},{"name":"Dongde Hou","slug":"dongde-hou","given":"Dongde","family":"Hou","affiliation":"Advanced Research Institute , Southwest University of Political Science&Law , Chongqing , China"}],"abstract":"Existing Unsupervised Domain Adaptation (UDA) methods typically attempt to perform knowledge transfer in a domain-invariant space explicitly or implicitly. In practice, however, the obtained features is often mixed with domain-specific information which causes performance degradation. To overcome this fundamental limitation, this article presents a novel independent feature decomposition and instance alignment method (IndUDA in short). Specifically, based on an invertible flow, we project the base features into a decomposed latent space with domain-invariant and domain-specific dimensions. To drive semantic decomposition independently, we then swap the domain-invariant part across source and target domain samples with the same category and require their inverted features are consistent in class-level with the original features.  By treating domain-specific information as noise, we replace it by Gaussian noise and further regularize source model training by instance alignment, i.e., requiring the base features close to the corresponding reconstructed features, respectively. Extensive experiment results demonstrate that our method achieves state-of-the-art performance on popular UDA benchmarks. The appendix and code are available at https://github.com/ayombeach/IndUDA.","doi":"10.24963/ijcai.2023/91","url":"https://ijcai.org/proceedings/2023/91","pdf":"https://ijcai.org/proceedings/2023/0091.pdf","markdown":"https://ijcai.org/proceedings/2023/91.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/91","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":91,"pages":{"first":819,"last":827},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Representation learning"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2019] Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, and Zhifeng Hao. 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