{"type":"ScholarlyArticle","title":"Self-adaptive Re-weighted Adversarial Domain Adaptation","authors":[{"name":"Shanshan Wang","slug":"shanshan-wang","given":"Shanshan","family":"Wang","affiliation":"Learning Intelligence & Vision Essential (LiVE) Group School of Microelectronics and Communication Engineering , Chongqing University , Chongqing , China"},{"name":"Lei Zhang","slug":"lei-zhang","given":"Lei","family":"Zhang","affiliation":"Learning Intelligence & Vision Essential (LiVE) Group School of Microelectronics and Communication Engineering , Chongqing University , Chongqing , China"}],"abstract":"Existing adversarial domain adaptation methods mainly consider the marginal distribution and these methods may lead to either under transfer or negative transfer. To address this problem, we present a self-adaptive re-weighted adversarial domain adaptation approach, which tries to enhance domain alignment from the perspective of conditional distribution. In order to promote positive transfer and combat negative transfer, we reduce the weight of the adversarial loss for aligned features while increasing the adversarial force for those poorly aligned measured by the conditional entropy. Additionally, triplet loss leveraging source samples and pseudo-labeled target samples is employed on the confusing domain. Such metric loss ensures the distance of the intra-class sample pairs closer than the inter-class pairs to achieve the class-level alignment. In this way, the high accurate pseudolabeled target samples and semantic alignment can be captured simultaneously in the co-training process. Our method achieved low joint error of the ideal source and target hypothesis. The expected target error can then be upper bounded following Ben-David’s theorem. Empirical evidence demonstrates that the proposed model outperforms state of the arts on standard domain adaptation datasets.","doi":"10.24963/ijcai.2020/440","url":"https://ijcai.org/proceedings/2020/440","pdf":"https://ijcai.org/proceedings/2020/0440.pdf","markdown":"https://ijcai.org/proceedings/2020/440.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/440","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":440,"pages":{"first":3181,"last":3187},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Classification","Machine Learning: Transfer, Adaptation, Multi-task Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24448/file_24448.m3u8","length_sec":297,"provider":"hls"}],"references":[{"position":1,"raw":"OfficeHome Ar→Cl Ar→Pr Ar→Rw Cl→Ar Cl→Pr Cl→Rw Pr→Ar Pr→Cl Pr→Rw Rw→Ar Rw→Cl Rw →Pr Avg. 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