Deep CORAL: Correlation Alignment for Deep Domain Adaptation
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CORAL penalizes differences in the means and covariances of the feature distributions for each domain.
“CORAL (Sun and Saenko, 2016), which penalizes differences in the means and covariances of the feature distributions (i.e., the distribution of last layer activations in a neural network) for each domain.”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Sun and Saenko, 2016
CORAL was originally proposed for domain adaptation, where it substantially improved performance on standard domain adaptation benchmarks, and was subsequently adapted for domain generalization.
“We note that CORAL was originally proposed in the context of domain adaptation (Sun and Saenko, 2016), where it was shown to substantially improve performance on standard domain adaptation benchmarks, and it was subsequently adapted for domain generalization (Gulrajani and Lopez-Paz, 2020).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Sun and Saenko, 2016
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“For instance, correlation alignment (CORAL) [28] and maximum mean discrepancy (MMD) [29] are used to reduce the distribution divergence between domains.”
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- Authors
- Sun, B., & Saenko, K.
- Affiliations
- University of Massachusetts Lowell (US)
- Boston University (US)
- Corresponding author
- Sun, Baochen
- ORCID
- Saenko, Kate
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 443-450
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2964288524
- Cited by
- 3,757
- References
- 5
- Citations by year
- 2026: 444 · 2025: 577 · 2024: 599 · 2023: 552 · 2022: 475 · 2021: 488 · 2020: 316 · 2019: 189 · 2018: 83 · 2017: 32 · 2016: 1
- FWCI
- 54.78
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (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 › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- deep domain adaptation, unsupervised domain adaptation, coral, correlation alignment, second-order statistics, domain shift, nonlinear transformation, layer activation, unlabeled target domain
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