Papers › Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Baochen Sun, Kate Saenko
Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions. Domain adaptation algorithms have been proposed to compensate for the degradation in performance due to domain shift. In this paper, we address the case when the target domain is unlabeled, requiring unsupervised adaptation. CORAL is a "frustratingly easy" unsupervised domain adaptation method that aligns the second-order statistics of the source and target distributions with a linear transformation. Here, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (Deep CORAL). Experiments on standard benchmark datasets show state-of-the-art performance.
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Code
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Code Syntology ran Syntology
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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 | NICO Animal | CORAL (Resnet-18) | Accuracy | 80.27 | #4 of 5 | Archive leaderboard | report |
| Domain Generalization | NICO Vehicle | CORAL (Resnet-18) | Accuracy | 71.64 | #5 of 5 | Archive leaderboard | report |
| Image Classification | iWildCam2020-WILDS | CORAL | Accuracy (Top-1) | 73.3 | #2 of 6 | 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.
Methods
Introduced by this paper: CORAL
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