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Deep CORAL: Correlation Alignment for Deep Domain Adaptation

arXiv 1607.01719 ↗First posted 6 Jul 2016cs.CV cs.AI cs.LGcited by 1,194

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Abstract (arXiv)

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.

Key facts. Cited by 1,194 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2016.

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Citing papers per year: 2021: 215; 2022: 238; 2023: 235; 2024: 197; 2025: 165; 2026: 144.

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  1. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  2. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  3. 2021SWAD: Domain Generalization by Seeking Flat Minimacited by 431
  4. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  5. 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentationcited by 259
  6. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  7. 2021Gradient Matching for Domain Generalizationcited by 258
  8. 2021RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whiteningcited by 213
  9. 2021Trustworthy AI: From Principles to Practicescited by 206
  10. 2022Improving Out-of-Distribution Robustness via Selective Augmentationcited by 202
  11. 2021SelfReg: Self-supervised Contrastive Regularization for Domain Generalizationcited by 189
  12. 2021Fishr: Invariant Gradient Variances for Out-of-Distribution Generalizationcited by 186
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  1. 2022Parameter-free Online Test-time Adaptationcited by 184
  2. 2022MIC: Masked Image Consistency for Context-Enhanced Domain Adaptationcited by 176
  3. 2021A Fine-Grained Analysis on Distribution Shiftcited by 171
  4. 2023Large Language Model Routing with Benchmark Datasetscited by 166
  5. 2021Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalizationcited by 144
  6. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  7. 2022GOOD: A Graph Out-of-Distribution Benchmarkcited by 140
  8. 2021On Calibration and Out-of-domain Generalizationcited by 134
  9. 2022Domain Generalization by Mutual-Information Regularization with Pre-trained Modelscited by 133
  10. 2021Disparities in Dermatology AI: Assessments Using Diverse Clinical Imagescited by 132
  11. 2021The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalizationcited by 131
  12. 2021CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptationcited by 129
  13. 2022Domain Adaptation via Prompt Learningcited by 128
  14. 2021AdaRNN: Adaptive Learning and Forecasting of Time Seriescited by 120
  15. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  16. 2021AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptationcited by 104
  17. 2021Model-Based Domain Generalizationcited by 103
  18. 2022Robust Mean Teacher for Continual and Gradual Test-Time Adaptationcited by 102
  19. 2023Sharpness-Aware Gradient Matching for Domain Generalizationcited by 102
  20. 2023TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptationcited by 101
  21. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  22. 2021Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perceptioncited by 97
  23. 2023CLIPood: Generalizing CLIP to Out-of-Distributionscited by 96
  24. 2021Towards a Theoretical Framework of Out-of-Distribution Generalizationcited by 94
  25. 2021Towards Principled Disentanglement for Domain Generalizationcited by 91
  26. 2021OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalizationcited by 86
  27. 2021Invariant Information Bottleneck for Domain Generalizationcited by 86
  28. 2021Exploiting Domain-Specific Features to Enhance Domain Generalizationcited by 86
  29. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
  30. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 85
  31. 2022Sparse Mixture-of-Experts are Domain Generalizable Learnerscited by 85
  32. 2022Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Timecited by 85
  33. 2022MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflictscited by 81
  34. 2021Cross-domain Contrastive Learning for Unsupervised Domain Adaptationcited by 75
  35. 2022WildNet: Learning Domain Generalized Semantic Segmentation from the Wildcited by 75
  36. 2024A Survey of Multimodal Large Language Model from A Data-centric Perspectivecited by 73
  37. 2021Break-It-Fix-It: Unsupervised Learning for Program Repaircited by 71
  38. 2023Discover and Cure: Concept-aware Mitigation of Spurious Correlationcited by 71

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