Domain Generalization by Mutual-Information Regularization with Pre-trained Models
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cha, J. | - |
dc.contributor.author | Lee, Kyungjae | - |
dc.contributor.author | Park, S. | - |
dc.contributor.author | Chun, S. | - |
dc.date.accessioned | 2023-03-08T05:10:06Z | - |
dc.date.available | 2023-03-08T05:10:06Z | - |
dc.date.issued | 2022-10 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.issn | 1611-3349 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/61189 | - |
dc.description.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. Code is available at https://github.com/kakaobrain/miro. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. | - |
dc.format.extent | 18 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | Domain Generalization by Mutual-Information Regularization with Pre-trained Models | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/978-3-031-20050-2_26 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.13683 LNCS, pp 440 - 457 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85142741908 | - |
dc.citation.endPage | 457 | - |
dc.citation.startPage | 440 | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 13683 LNCS | - |
dc.type.docType | Conference Paper | - |
dc.publisher.location | 미국 | - |
dc.description.journalRegisteredClass | scopus | - |
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