mlsanityData to 7 Oct 2026

Learning to Learn Single Domain Generalization

arXiv 2003.13216 ↗First posted 30 Mar 2020cs.CVcited by 295

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Abstract (arXiv)

We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training. We propose a new method named adversarial domain augmentation to solve this Out-of-Distribution (OOD) generalization problem. The key idea is to leverage adversarial training to create "fictitious" yet "challenging" populations, from which a model can learn to generalize with theoretical guarantees. To facilitate fast and desirable domain augmentation, we cast the model training in a meta-learning scheme and use a Wasserstein Auto-Encoder (WAE) to relax the widely used worst-case constraint. Detailed theoretical analysis is provided to testify our formulation, while extensive experiments on multiple benchmark datasets indicate its superior performance in tackling single domain generalization.

Key facts. Cited by 295 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 57; 2022: 52; 2023: 75; 2024: 59; 2025: 40; 2026: 12.

295 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. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  5. 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
  6. 2021SMIL: Multimodal Learning with Severely Missing Modalitycited by 243
  7. 2022Improving Out-of-Distribution Robustness via Selective Augmentationcited by 202
  8. 2021Learning to Diversify for Single Domain Generalizationcited by 170
  9. 2021FSDR: Frequency Space Domain Randomization for Domain Generalizationcited by 163
  10. 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
  11. 2022Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalizationcited by 122
  12. 2021Causality-inspired Single-source Domain Generalization for Medical Image Segmentationcited by 118
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  1. 2021Model-Based Domain Generalizationcited by 103
  2. 2021Progressive Domain Expansion Network for Single Domain Generalizationcited by 96
  3. 2022Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoningcited by 90
  4. 2023CLIP the Gap: A Single Domain Generalization Approach for Object Detectioncited by 87
  5. 2021OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalizationcited by 86
  6. 2021Invariant Information Bottleneck for Domain Generalizationcited by 86
  7. 2021Adversarially Adaptive Normalization for Single Domain Generalizationcited by 85
  8. 2022Sparse Mixture-of-Experts are Domain Generalizable Learnerscited by 85
  9. 2021Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalizationcited by 83
  10. 2021Generalizing Graph Neural Networks on Out-Of-Distribution Graphscited by 69
  11. 2022Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentationcited by 69
  12. 2023PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalizationcited by 59
  13. 2021SITA: Single Image Test-time Adaptationcited by 57
  14. 2021MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptationcited by 54
  15. 2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
  16. 2021Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorizationcited by 50
  17. 2021A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?cited by 48
  18. 2021Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalizationcited by 45
  19. 2021Semi-Supervised Domain Generalization with Stochastic StyleMatchcited by 43
  20. 20213D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detectioncited by 43
  21. 2023Meta-causal Learning for Single Domain Generalizationcited by 43
  22. 2023In Search of Lost Online Test-time Adaptation: A Surveycited by 43
  23. 2021Uncertainty-guided Model Generalization to Unseen Domainscited by 41
  24. 2021Towards Unsupervised Domain Generalizationcited by 40
  25. 2021Feature Stylization and Domain-aware Contrastive Learning for Domain Generalizationcited by 40
  26. 2023Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Modelscited by 40
  27. 2022ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networkscited by 39
  28. 2022PASTA: Proportional Amplitude Spectrum Training Augmentation for Syn-to-Real Domain Generalizationcited by 38
  29. 2022Attention Consistency on Visual Corruptions for Single-Source Domain Generalizationcited by 37
  30. 2023StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalizationcited by 36
  31. 2021Domain Generalization via Inference-time Label-Preserving Target Projectionscited by 35
  32. 2023Anomaly Detection under Distribution Shiftcited by 35
  33. 2023Progressive Random Convolutions for Single Domain Generalizationcited by 33
  34. 2023Collaborating Foundation Models for Domain Generalized Semantic Segmentationcited by 33
  35. 2022Temporal Domain Generalization with Drift-Aware Dynamic Neural Networkscited by 32
  36. 2021Transformer-Based Source-Free Domain Adaptationcited by 30
  37. 2023Neuron Structure Modeling for Generalizable Remote Physiological Measurementcited by 29
  38. 2023Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimizationcited by 29

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