The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Optimal Transport for Domain Adaptation
Open accessAbstract
Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system. Among the many strategies proposed, finding domain-invariant representations has shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labeled samples of the same class in the source domain to remain close during transport. This way, we exploit at the same time the labeled samples in the source and the distributions observed in both domains. Experiments on toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches. In addition, numerical experiments show that our approach leads to better performances on domain invariant deep learning features and can be easily adapted to the semi-supervised case where few labeled samples are available in the target domain.
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Cited by
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239
Self-citationA Review of Domain Adaptation without Target Labels
Kouw, W. M., & Loog, M. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2945942 · Cited by 613
Transfer adaptation learning: A decade survey
Zhang, L., & Gao, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183326 · Cited by 314
Alignment and integration of spatial transcriptomics data
Zeira, R., Land, M., Strzalkowski, A., & Raphael, B. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01459-6 · Cited by 298
On Learning Invariant Representation for Domain Adaptation
Zhao, H., Combes, R. T. D., Zhang, K., & Gordon, G. J. (2019) · arXiv · doi:10.48550/arxiv.1901.09453 · Cited by 134
Metadata
- Authors
- Courty, N., Flamary, R., Tuia, D., & Rakotomamonjy, A.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2017-09-01
- Volume
- 39
- Issue
- 9
- Pages
- 1853-1865
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W1594039573
- Cited by
- 989
- References
- 46
- FWCI
- 44.81
- Type
- journal article
- Language
- en
- Open access
- green
- Funders
- Swiss National Science Foundation (PP00P2-150593)
- CNRS PEPS Fascido program
- Topase project
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- Courty, N., Flamary, R., Tuia, D., & Rakotomamonjy, A. (2017). Optimal Transport for Domain Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2016.2615921
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