mlsanityData to 7 Oct 2026

Domain Generalization via Invariant Feature Representation

arXiv 1301.2115 ↗First posted 10 Jan 2013stat.ML cs.LGcited by 622

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Abstract (arXiv)

This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains? We propose Domain-Invariant Component Analysis (DICA), a kernel-based optimization algorithm that learns an invariant transformation by minimizing the dissimilarity across domains, whilst preserving the functional relationship between input and output variables. A learning-theoretic analysis shows that reducing dissimilarity improves the expected generalization ability of classifiers on new domains, motivating the proposed algorithm. Experimental results on synthetic and real-world datasets demonstrate that DICA successfully learns invariant features and improves classifier performance in practice.

Key facts. Cited by 622 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jan 2013.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 121; 2022: 132; 2023: 147; 2024: 104; 2025: 73; 2026: 45.

622 papers.

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  1. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  2. 2021Domain Generalization: A Surveycited by 760
  3. 2021Domain Generalization with MixStylecited by 563
  4. 2021Just Train Twice: Improving Group Robustness without Training Group Informationcited by 522
  5. 2022Continual Test-Time Domain Adaptationcited by 520
  6. 2021SWAD: Domain Generalization by Seeking Flat Minimacited by 431
  7. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  8. 2022Last Layer Re-Training is Sufficient for Robustness to Spurious Correlationscited by 379
  9. 2021MEMO: Test Time Robustness via Adaptation and Augmentationcited by 376
  10. 2021A Fourier-based Framework for Domain Generalizationcited by 287
  11. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  12. 2021Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalizationcited by 220
38 more
  1. 2021RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whiteningcited by 213
  2. 2022Improving Out-of-Distribution Robustness via Selective Augmentationcited by 202
  3. 2021SelfReg: Self-supervised Contrastive Regularization for Domain Generalizationcited by 189
  4. 2021Fishr: Invariant Gradient Variances for Out-of-Distribution Generalizationcited by 186
  5. 2023Robust Test-Time Adaptation in Dynamic Scenarioscited by 185
  6. 2023ZipIt! Merging Models from Different Tasks without Trainingcited by 178
  7. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 177
  8. 2021Learning to Diversify for Single Domain Generalizationcited by 170
  9. 2021FSDR: Frequency Space Domain Randomization for Domain Generalizationcited by 163
  10. 2022Handling Distribution Shifts on Graphs: An Invariance Perspectivecited by 163
  11. 2021Hand-Object Contact Consistency Reasoning for Human Grasps Generationcited by 160
  12. 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
  13. 2022Diverse Weight Averaging for Out-of-Distribution Generalizationcited by 148
  14. 2022SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptationcited by 146
  15. 2024WARM: On the Benefits of Weight Averaged Reward Modelscited by 145
  16. 2021Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalizationcited by 144
  17. 2022Causality Inspired Representation Learning for Domain Generalizationcited by 136
  18. 2022GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Partscited by 135
  19. 2022Domain Generalization by Mutual-Information Regularization with Pre-trained Modelscited by 133
  20. 2021AdaRNN: Adaptive Learning and Forecasting of Time Seriescited by 120
  21. 2021Causality-inspired Single-source Domain Generalization for Medical Image Segmentationcited by 118
  22. 2021Open Domain Generalization with Domain-Augmented Meta-Learningcited by 115
  23. 2021Measure and Improve Robustness in NLP Models: A Surveycited by 107
  24. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 105
  25. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
  26. 2021Model-Based Domain Generalizationcited by 103
  27. 2022Robust Mean Teacher for Continual and Gradual Test-Time Adaptationcited by 102
  28. 2023Sharpness-Aware Gradient Matching for Domain Generalizationcited by 102
  29. 2021Progressive Domain Expansion Network for Single Domain Generalizationcited by 96
  30. 2023CLIPood: Generalizing CLIP to Out-of-Distributionscited by 96
  31. 2021Towards a Theoretical Framework of Out-of-Distribution Generalizationcited by 94
  32. 2021Towards Principled Disentanglement for Domain Generalizationcited by 91
  33. 2023Generative models improve fairness of medical classifiers under distribution shiftscited by 90
  34. 2021OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalizationcited by 86
  35. 2021Invariant Information Bottleneck for Domain Generalizationcited by 86
  36. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
  37. 2021Causal Attention for Unbiased Visual Recognitioncited by 80
  38. 2022Semantic-Aware Domain Generalized Segmentationcited by 78

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