{"type":"ScholarlyArticle","title":"Learning Robust Multi-view Representation Using Dual-masked VAEs","authors":[{"name":"Jiedong Wang","slug":"jiedong-wang","given":"Jiedong","family":"Wang","affiliation":""},{"name":"Kai Guo","slug":"kai-guo","given":"Kai","family":"Guo","affiliation":""},{"name":"Peng Hu","slug":"peng-hu","given":"Peng","family":"Hu","affiliation":""},{"name":"Xi Peng","slug":"xi-peng","given":"Xi","family":"Peng","affiliation":""},{"name":"Hao Wang","slug":"hao-wang","given":"Hao","family":"Wang","affiliation":""}],"abstract":"Most existing multi-view representation learning methods assume view-completeness and noise-free data. However, such assumptions are strong in real-world applications. Despite advances in methods tailored to view-missing or noise problems individually, a one-size-fits-all approach that concurrently addresses both remains unavailable. To this end, we propose a holistic method, called Dual-masked Variational Autoencoders (DualVAE), which aims at learning robust multi-view representation. The DualVAE exhibits an innovative amalgamation of dual-masked prediction, mixture-of-experts learning, representation disentangling, and a joint loss function in wrapping up all components. The key novelty lies in the dual-masked (view-mask and patch-mask) mechanism to mimic missing views and noisy data. Extensive experiments on four multi-view datasets show the effectiveness of the proposed method and its superior performance in comparison to baselines. The code is available at https://github.com/XLearning-SCU/2025-IJCAI-DualVAE.","doi":"10.24963/ijcai.2025/701","url":"https://ijcai.org/proceedings/2025/701","pdf":"https://ijcai.org/proceedings/2025/0701.pdf","markdown":"https://ijcai.org/proceedings/2025/701.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/701","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":701,"pages":{"first":6298,"last":6306},"keywords":["Machine Learning: ML: Clustering","Machine Learning: ML: Representation learning","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}