{"type":"ScholarlyArticle","title":"Optimal Graph Learning and Nuclear Norm Maximization for Deep Cross-Domain Robust Label Propagation","authors":[{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University"},{"name":"Hanyang Li","slug":"hanyang-li","given":"Hanyang","family":"Li","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University"},{"name":"Ke Shi","slug":"ke-shi","given":"Ke","family":"Shi","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences"},{"name":"Chao Huang","slug":"chao-huang","given":"Chao","family":"Huang","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University"},{"name":"Yang Cao","slug":"yang-cao","given":"Yang","family":"Cao","affiliation":"Department of Computer Science , Tokyo Institute of Technology"},{"name":"Cong Wang","slug":"cong-wang","given":"Cong","family":"Wang","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University; Department of Computing , The Hong Kong Polytechnic University"},{"name":"Xiaochun Cao","slug":"xiaochun-cao","given":"Xiaochun","family":"Cao","affiliation":"School of Cyber Science and Technology , Shenzhen Campus of Sun Yat-sen University"}],"abstract":"Domain adaptation aims to achieve label transfer from a labeled source domain to an unlabeled target domain, where the two domains exhibit different distributions. Existing methods primarily concentrate on designing a feature extractor to learn better domain-invariant features, along with developing an effective classifier for reliable predictions. In this paper, we introduce optimal graph learning to generate a cross-domain graph that effectively connects the two domains, and two domain-specific graphs to capture domain-specific structures. On the one hand, we incorporate the three graphs into the label propagation (LP) classifier to enhance its robustness to distribution difference. On the other hand, we leverage the three graphs to introduce graph embedding losses, promoting the learning of locally discriminative and domain-invariant features. Furthermore, we maximize the nuclear norm of predictions in LP to enhance class diversity, thereby improving its robustness to class imbalance problem. Correspondingly, we develop an efficient algorithm to solve the associated optimization problem. Finally, we integrate the proposed LP and graph embedding losses into a deep neural network, resulting in our proposed deep cross-domain robust LP. Extensive experiments conducted on three cross-domain benchmark datasets demonstrate that our proposed approach could outperform existing state-of-the-art domain adaptation methods.","doi":"10.24963/ijcai.2024/156","url":"https://ijcai.org/proceedings/2024/156","pdf":"https://ijcai.org/proceedings/2024/0156.pdf","markdown":"https://ijcai.org/proceedings/2024/156.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/156","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":"Computer Vision","track":"Main Track","number":156,"pages":{"first":1407,"last":1415},"keywords":["Machine Learning: ML: Feature extraction, selection and dimensionality reduction","Machine Learning: ML: Classification","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Bousmalis et al., 2016] Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Du- mitru Erhan. 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