{"type":"ScholarlyArticle","title":"Reconfigurability-Aware Selection for Contrastive Active Domain Adaptation","authors":[{"name":"Zeyu Zhang","slug":"zeyu-zhang","given":"Zeyu","family":"Zhang","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering ( Jilin University) , Ministry of Education , China; College of Computer Science and Technology , Jilin University , China"},{"name":"Chun Shen","slug":"chun-shen","given":"Chun","family":"Shen","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering ( Jilin University) , Ministry of Education , China; College of Computer Science and Technology , Jilin University , China; College of Software , Jilin University , China"},{"name":"Shuai Lü","slug":"shuai-lu","given":"Shuai","family":"Lu","affiliation":""},{"name":"Shaojie Zhang","slug":"shaojie-zhang","given":"Shaojie","family":"Zhang","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering ( Jilin University) , Ministry of Education , China; College of Computer Science and Technology , Jilin University , China"}],"abstract":"Active domain adaptation (ADA) aims to label a small portion of target samples to drastically improve the adaptation performance. The existing ADA methods mostly rely on the output of domain discriminator or the original prediction probability to design sample selection strategies and do not fully explore the semantic information of source and target domain features, which may lead to selecting the valueless target samples. Moreover, most of them require complex network structures (such as introducing additional domain discriminator, multiple classifiers, or loss predictors) and multiple query functions. In this work, we propose a concise but effective ADA method called Reconfigurability-Aware Selection for Contrastive active domain adaptation (RASC). With the reconfigurability-aware sample selection strategy, RASC can select the most valuable target samples for annotation in the presence of domain shift. To better utilize the selected target samples, we further design a contrastive learning-based gradual active domain adaptation framework. In addition, we propose a variant of RASC called RASC-Ob, which uses a simpler sample annotation method and supplements the learning of misclassified samples. Extensive experimental results on multiple benchmarks demonstrate the superiority of RASC.","doi":"10.24963/ijcai.2024/613","url":"https://ijcai.org/proceedings/2024/613","pdf":"https://ijcai.org/proceedings/2024/0613.pdf","markdown":"https://ijcai.org/proceedings/2024/613.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/613","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":613,"pages":{"first":5545,"last":5553},"keywords":["Machine Learning: ML: Semi-supervised learning","Machine Learning: ML: Multi-task and transfer learning"],"videos":[],"references":[{"position":1,"raw":"[Ash et al., 2020] Jordan T. 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