Papers › Domain Generalization by Mutual-Information Regularization with Pre-trained Models
Domain Generalization by Mutual-Information Regularization with Pre-trained Models
Junbum Cha, Kyungjae Lee, Sungrae Park, Sanghyuk Chun
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.
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Code
Syntology Ran 11 of 13 code samples harvested from 1 repository linked to this paper; 2 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · our draft was wrong; 8 ran with no contract checked.
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Code Syntology ran Syntology
13 samples harvested; 11 ran; 1 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 0 of the 13 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from kakaobrain/miro. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Generalization | DomainNet | MIRO (RegNetY-16GF, SWAD) | Average Accuracy | 60.7 | #11 of 38 | Archive leaderboard | report |
| Domain Generalization | DomainNet | MIRO (ResNet-50, SWAD) | Average Accuracy | 47.0 | #27 of 38 | Archive leaderboard | report |
| Domain Generalization | Office-Home | MIRO (RegNetY-16GF, SWAD) | Average Accuracy | 83.3 | #12 of 45 | Archive leaderboard | report |
| Domain Generalization | Office-Home | MIRO (ResNet-50, SWAD) | Average Accuracy | 72.4 | #25 of 45 | Archive leaderboard | report |
| Domain Generalization | PACS | MIRO (RegNetY-16GF, SWAD) | Average Accuracy | 96.8 | #9 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | MIRO (ResNet-50, SWAD) | Average Accuracy | 88.4 | #28 of 133 | Archive leaderboard | report |
| Domain Generalization | TerraIncognita | MIRO (RegNetY-16GF, SWAD) | Average Accuracy | 64.3 | #3 of 30 | Archive leaderboard | report |
| Domain Generalization | TerraIncognita | MIRO (ResNet-50, SWAD) | Average Accuracy | 52.9 | #16 of 30 | Archive leaderboard | report |
| Domain Generalization | VLCS | MIRO (RegNetY-16GF, SWAD) | Average Accuracy | 81.7 | #16 of 37 | Archive leaderboard | report |
| Domain Generalization | VLCS | MIRO (ResNet-50, SWAD) | Average Accuracy | 79.6 | #24 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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