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Domain Generalization via Multidomain Discriminant Analysis

arXiv 1907.11216 ↗First posted 25 Jul 2019stat.ML cs.LGcited by 69

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Abstract (arXiv)

Domain generalization (DG) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains. This problem is ubiquitous in practice since the distributions of the target data may rarely be identical to those of the source data. In this paper, we propose Multidomain Discriminant Analysis (MDA) to address DG of classification tasks in general situations. MDA learns a domain-invariant feature transformation that aims to achieve appealing properties, including a minimal divergence among domains within each class, a maximal separability among classes, and overall maximal compactness of all classes. Furthermore, we provide the bounds on excess risk and generalization error by learning theory analysis. Comprehensive experiments on synthetic and real benchmark datasets demonstrate the effectiveness of MDA.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 10; 2023: 24; 2024: 13; 2025: 5; 2026: 3.

69 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. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  4. 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
  5. 2021Model-Based Domain Generalizationcited by 103
  6. 2021Exploiting Domain-Specific Features to Enhance Domain Generalizationcited by 86
  7. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
  8. 2021Adaptive Methods for Real-World Domain Generalizationcited by 66
  9. 2023Improved Test-Time Adaptation for Domain Generalizationcited by 45
  10. 2021Towards Unsupervised Domain Generalizationcited by 40
  11. 2021Domain Generalization via Inference-time Label-Preserving Target Projectionscited by 35
  12. 2023Anomaly Detection under Distribution Shiftcited by 35
38 more
  1. 2022Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalizationcited by 34
  2. 2021An Online Learning Approach to Interpolation and Extrapolation in Domain Generalizationcited by 30
  3. 2024Credal Learning Theorycited by 30
  4. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  5. 2023Flatness-Aware Minimization for Domain Generalizationcited by 27
  6. 2024Rethinking Multi-domain Generalization with A General Learning Objectivecited by 23
  7. 2023MADG: Margin-based Adversarial Learning for Domain Generalizationcited by 22
  8. 2023DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networkscited by 20
  9. 2021Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inferencecited by 18
  10. 2021Variational Disentanglement for Domain Generalizationcited by 18
  11. 2022Intra-Source Style Augmentation for Improved Domain Generalizationcited by 17
  12. 2022Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruningcited by 17
  13. 2023Domain Generalization via Rationale Invariancecited by 16
  14. 2023Modality-Agnostic Debiasing for Single Domain Generalizationcited by 14
  15. 2023BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentationcited by 14
  16. 2023Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issuescited by 12
  17. 2023TFS-ViT: Token-Level Feature Stylization for Domain Generalizationcited by 10
  18. 2023SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalizationcited by 9
  19. 2022The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimizationcited by 8
  20. 2023Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalizationcited by 8
  21. 2024LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalizationcited by 8
  22. 2025Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'cited by 8
  23. 2022Breaking Correlation Shift via Conditional Invariant Regularizercited by 7
  24. 2023Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalizationcited by 7
  25. 2023Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognitioncited by 6
  26. 2024Domain Generalization with Small Datacited by 6
  27. 2022Domain Generalisation for Object Detection under Covariate and Concept Shiftcited by 5
  28. 2023Domain-aware Triplet loss in Domain Generalizationcited by 5
  29. 2023AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Predictioncited by 5
  30. 2024FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain Generalizationcited by 5
  31. 2023INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalizationcited by 4
  32. 2024A Causal Inspired Early-Branching Structure for Domain Generalizationcited by 4
  33. 2024MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignmentcited by 4
  34. 2023Domain Generalization for Mammographic Image Analysis with Contrastive Learningcited by 3
  35. 2024Towards a Universal 3D Medical Multi-modality Generalization via Learning Personalized Invariant Representationcited by 3
  36. 2022Feature-Distribution Perturbation and Calibration for Generalized Person ReIDcited by 2
  37. 2023ShiftKD: Benchmarking Knowledge Distillation under Distribution Shiftcited by 2
  38. 2024Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learningcited by 2

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