{"id":3851,"date":"2021-05-14T10:00:02","date_gmt":"2021-05-14T09:00:02","guid":{"rendered":"https:\/\/www.ceessnoek.info\/?p=3851"},"modified":"2021-05-14T10:03:07","modified_gmt":"2021-05-14T09:03:07","slug":"icml-2021-a-bit-more-bayesian-domain-invariant-learning-with-uncertainty","status":"publish","type":"post","link":"https:\/\/www.ceessnoek.info\/index.php\/icml-2021-a-bit-more-bayesian-domain-invariant-learning-with-uncertainty\/","title":{"rendered":"ICML 2021: A Bit More Bayesian: Domain-Invariant Learning with Uncertainty"},"content":{"rendered":"\n

The ICML 2021 paper \u201cA Bit More Bayesian: Domain-Invariant Learning with Uncertaintyce<\/em>\u201d by Zehao Xiao, Jiayi Shen, Xiantong Zhen, Ling Shao and Cees Snoek is\u00a0now available<\/a>. Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with a probabilistic framework based on variational Bayesian inference, by incorporating uncertainty into neural network weights. We couple domain invariance in a probabilistic formula with the variational Bayesian inference. This enables us to explore domain-invariant learning in a principled way. Specifically, we derive domain-invariant representations and classifiers, which are jointly established in a two-layer Bayesian neural network. We empirically demonstrate the effectiveness of our proposal on four widely used cross-domain visual recognition benchmarks. Ablation studies validate the synergistic benefits of our Bayesian treatment when jointly learning domain-invariant representations and classifiers for domain generalization. Further, our method consistently delivers state-of-the-art mean accuracy on all benchmarks. <\/p>\n\n\n\n

\"\"<\/a><\/figure>\n","protected":false},"excerpt":{"rendered":"

The ICML 2021 paper \u201cA Bit More Bayesian: Domain-Invariant Learning with Uncertaintyce\u201d by Zehao Xiao, Jiayi Shen, Xiantong Zhen, Ling Shao and Cees Snoek is\u00a0now available. Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with a […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-3851","post","type-post","status-publish","format-standard","hentry","category-science"],"yoast_head":"\nICML 2021: A Bit More Bayesian: Domain-Invariant Learning with Uncertainty - Cees Snoek<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ceessnoek.info\/index.php\/icml-2021-a-bit-more-bayesian-domain-invariant-learning-with-uncertainty\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ICML 2021: A Bit More Bayesian: Domain-Invariant Learning with Uncertainty - Cees Snoek\" \/>\n<meta property=\"og:description\" content=\"The ICML 2021 paper \u201cA Bit More Bayesian: Domain-Invariant Learning with Uncertaintyce\u201d by Zehao Xiao, Jiayi Shen, Xiantong Zhen, Ling Shao and Cees Snoek is\u00a0now available. Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. 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