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Domain Generalization by Mutual-Information Regularization with Pre-trained Models

2022-03-21 08:07:46
Junbum Cha, Kyungjae Lee, Sungrae Park, Sanghyuk Chun
arXiv_CV
   
Abstract

Domain generalization (DG) aims to learn a generalized model to an unseen target domain using only limited source domains. Previous attempts to DG fail to learn domain-invariant representations only from the source domains due to the significant domain shifts between training and test domains. Instead, we re-formulate the DG objective using mutual information with the oracle model, a model generalized to any possible domain. We derive a tractable variational lower bound via approximating the oracle model by a pre-trained model, called Mutual Information Regularization with Oracle (MIRO). Our extensive experiments show that MIRO significantly improves the out-of-distribution performance. Furthermore, our scaling experiments show that the larger the scale of the pre-trained model, the greater the performance improvement of MIRO. Source code is available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2203.10789

PDF

https://arxiv.org/pdf/2203.10789.pdf

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