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 https://github.com/kakaobrain/miro.
@article{arxiv.2203.10789,
title = {Domain Generalization by Mutual-Information Regularization with Pre-trained Models},
author = {Junbum Cha and Kyungjae Lee and Sungrae Park and Sanghyuk Chun},
journal= {arXiv preprint arXiv:2203.10789},
year = {2022}
}