{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:53Z","timestamp":1758672893732,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/701","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"6298-6306","source":"Crossref","is-referenced-by-count":0,"title":["Learning Robust Multi-view Representation Using Dual-masked VAEs"],"prefix":"10.24963","author":[{"given":"Jiedong","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, China"},{"name":"National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:34:53Z","timestamp":1758627293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/701"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/701","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}