{"type":"ScholarlyArticle","title":"Bayesian Uncertainty Matching for Unsupervised Domain Adaptation","authors":[{"name":"Jun Wen","slug":"jun-wen","given":"Jun","family":"Wen","affiliation":"Qiushi Academy for Advanced Studies , Zhejiang University , Hangzhou , China; College of Computer Science and Technology , Zhejiang University , Hangzhou , China"},{"name":"Nenggan Zheng","slug":"nenggan-zheng","given":"Nenggan","family":"Zheng","affiliation":"Qiushi Academy for Advanced Studies , Zhejiang University , Hangzhou , China; College of Computer Science and Technology , Zhejiang University , Hangzhou , China"},{"name":"Junsong Yuan","slug":"junsong-yuan","given":"Junsong","family":"Yuan","affiliation":"Computer Science and Engineering Department , State University of New York at Buffalo"},{"name":"Zhefeng Gong","slug":"zhefeng-gong","given":"Zhefeng","family":"Gong","affiliation":"Department of Neurobiology , Zhejiang University School of Medicine , Hangzhou , China"},{"name":"Changyou Chen","slug":"changyou-chen","given":"Changyou","family":"Chen","affiliation":"Computer Science and Engineering Department , State University of New York at Buffalo"}],"abstract":"Domain adaptation is an important technique to alleviate performance degradation caused by domain shift, e.g., when training and test data come from different domains. Most existing deep adaptation methods focus on reducing domain shift by matching marginal feature distributions through deep transformations on the input features, due to the unavailability of target domain labels. We show that domain shift may still exist via label distribution shift at the classifier, thus deteriorating model performances. To alleviate this issue, we propose an approximate joint distribution matching scheme by exploiting prediction uncertainty. Specifically, we use a Bayesian neural network to quantify prediction uncertainty of a classifier. By imposing distribution matching on both features and labels (via uncertainty), label distribution mismatching in source and target data is effectively alleviated, encouraging the classifier to produce consistent predictions across domains. We also propose a few techniques to improve our method by adaptively reweighting domain adaptation loss to achieve nontrivial distribution matching and stable training. Comparisons with state of the art unsupervised domain adaptation methods on three popular benchmark datasets demonstrate the superiority of our approach, especially on the effectiveness of alleviating negative transfer.","doi":"10.24963/ijcai.2019/534","url":"https://ijcai.org/proceedings/2019/534","pdf":"https://ijcai.org/proceedings/2019/0534.pdf","markdown":"https://ijcai.org/proceedings/2019/534.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/534","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":534,"pages":{"first":3849,"last":3855},"keywords":["Uncertainty in AI: Bayesian Networks","Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Adversarial Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Ben-David et al., 2010] Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jen- nifer Wortman Vaughan. 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