The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Return of Frustratingly Easy Domain Adaptation
Open accessAbstract
Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being ``frustratingly easy'' to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple--it can be implemented in four lines of Matlab code--CORAL performs remarkably well in extensive evaluations on standard benchmark datasets.
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“The core idea is to mathematically transform the EEG data from different subjects into a shared feature space, thereby reducing the distributional discrepancies caused by inter-subject variability and making the data more comparable for model training (Sun et al., 2016).”
From Transfer learning for EEG-based BCIs: a comparative evaluation and optimization of data alignment methods (2026) · cites this paper as Sun et al., 2016
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“A key strategy within inter-subject TL for EEG-BCIs is data alignment.”
From Transfer learning for EEG-based BCIs: a comparative evaluation and optimization of data alignment methods (2026) · cites this paper as Sun et al., 2016
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Cites
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Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
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Unsupervised Domain Adaptation by BackpropagationPreprint
Ganin, Y., & Lempitsky, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7495 · Cited by 5,441
Domain Adaptation via Transfer Component Analysis
Pan, S. J., Tsang, I. W.-H., Kwok, J. T.-Y., & Yang, Q. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2091281 · Cited by 4,809
Learning Transferable Features with Deep Adaptation Networks
Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv · doi:10.48550/arxiv.1502.02791 · Cited by 4,505
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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SUN database: Large-scale scene recognition from abbey to zoo
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Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., & Darrell, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_16 · Cited by 2,847
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Research works printed but not linked: 1
- Tommasi, T., and Tuytelaars, T. 2014. A testbed for crossdataset analysis. In ECCV TASK-CV Workshop .
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Wu, H., & Wu, D. (2022) · Brain Science Advances · doi:10.26599/bsa.2022.9050001 · Cited by 7
Cross-domain person re-identification using Dual Generation Learning in camera sensor networks
Zhang, Z., Wang, Y., & Liu, S. (2020) · Ad Hoc Networks · doi:10.1016/j.adhoc.2019.102019 · Cited by 5
Vishwanath, M., Cao, S., Dutt, N., Rahmani, A. M., Lim, M. M., & Cao, H. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107948 · Cited by 3
Ahmed, S. G., Ambali Parambil, M. M., Damseh, R., Bouktif, S., Alnajjar, F., & Belkacem, A. N. (2026) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2026.1840121 · Cited by 2
Towards Out-Of-Distribution Generalization: A Survey
Liu, J., Shen, Z., He, Y., Zhang, X., Xu, R., Yu, H., & Cui, P. (2021) · arXiv · doi:10.48550/arxiv.2108.13624 · Cited by 253
Metadata
- Authors
- Sun, B., Feng, J., & Saenko, K.
- Affiliations
- University of Massachusetts Lowell (US)
- University of California, Berkeley (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2016-03-02
- Volume
- 30
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2963275094
- Cited by
- 2,004
- References
- 44
- Citations by year
- 2026: 113 · 2025: 184 · 2024: 246 · 2023: 274 · 2022: 236 · 2021: 315 · 2020: 254 · 2019: 186 · 2018: 125 · 2017: 50 · 2016: 17
- FWCI
- 66.62
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1451244, 1212928)
- Nvidia (Tesla K40)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- unsupervised domain adaptation, domain shift, coral, unlabeled target data
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