A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

• URL: http://arxiv.org/abs/2105.04030v1
• Date: Sun, 9 May 2021 21:33:27 GMT
• Title: A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
• Authors: Zehao Xiao, Jiayi Shen, Xiantong Zhen, Ling Shao, Cees G. M. Snoek
• Abstract summary: 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. We derive domain-invariant representations and classifiers, which are jointly established in a two-layer Bayesian neural network.
• Score: 111.22588110362705
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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