mlsanityData to 7 Oct 2026

Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach

arXiv 2108.02888 ↗First posted 5 Aug 2021cs.CVcited by 18

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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 Meta-Learning based Adversarial Domain Augmentation to solve this Out-of-Domain 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 to relax the widely used worst-case constraint. We further improve our method by integrating uncertainty quantification for efficient domain generalization. Extensive experiments on multiple benchmark datasets indicate its superior performance in tackling single domain generalization.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 57 of its 96 references that are in the map. First posted on arXiv 5 Aug 2021.

Builds on

57 of 96 references.

References · 57

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  5. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  6. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  7. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  8. 2013Intriguing properties of neural networkscited by 5,114
  9. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  10. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  11. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  12. 2016Wide Residual Networkscited by 3,057
38 more
  1. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  2. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  3. 2016Matching Networks for One Shot Learningcited by 2,246
  4. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  5. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
  6. 2019Learning Robust Global Representations by Penalizing Local Predictive Powercited by 1,010
  7. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  8. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 918
  9. 2019AugMix: A Simple Data Processing Method to Improve Robustness and Uncertaintycited by 897
  10. 2017Learning to Generalize: Meta-Learning for Domain Generalizationcited by 762
  11. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 741
  12. 2013Domain Generalization via Invariant Feature Representationcited by 622
  13. 2016Learning to learn by gradient descent by gradient descentcited by 616
  14. 2016Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networkscited by 541
  15. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 511
  16. 2018Generalizing to Unseen Domains via Adversarial Data Augmentationcited by 451
  17. 2019Domain Generalization by Solving Jigsaw Puzzlescited by 449
  18. 2019Domain Generalization via Model-Agnostic Learning of Semantic Featurescited by 405
  19. 2017Wasserstein Auto-Encoderscited by 371
  20. 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 333
  21. 2017Unified Deep Supervised Domain Adaptation and Generalizationcited by 322
  22. 2020Learning to Learn Single Domain Generalizationcited by 295
  23. 2016Learning from Simulated and Unsupervised Images through Adversarial Trainingcited by 287
  24. 2018Generalizing Across Domains via Cross-Gradient Trainingcited by 268
  25. 2018Probabilistic Model-Agnostic Meta-Learningcited by 254
  26. 2015Domain Generalization for Object Recognition with Multi-task Autoencoderscited by 253
  27. 2018A DIRT-T Approach to Unsupervised Domain Adaptationcited by 215
  28. 2019Semantic Graph Convolutional Networks for 3D Human Pose Regressioncited by 204
  29. 2016Learning to Optimizecited by 195
  30. 2018Bayesian Model-Agnostic Meta-Learningcited by 194
  31. 2017Generate To Adapt: Aligning Domains using Generative Adversarial Networkscited by 183
  32. 2018Recasting Gradient-Based Meta-Learning as Hierarchical Bayescited by 180
  33. 2017Image to Image Translation for Domain Adaptationcited by 171
  34. 2018Disentangling Adversarial Robustness and Generalizationcited by 171
  35. 2017Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Graspingcited by 138
  36. 2020Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustnesscited by 121
  37. 2017Few-Shot Adversarial Domain Adaptationcited by 111
  38. 2017Learning to Compose Domain-Specific Transformations for Data Augmentationcited by 91

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 6; 2025: 2; 2026: 2.

18 papers.

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  1. 2023Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentationcited by 29
  2. 2023Frequency-mixed Single-source Domain Generalization for Medical Image Segmentationcited by 20
  3. 2023Modality-Agnostic Debiasing for Single Domain Generalizationcited by 14
  4. 2023Are Data-driven Explanations Robust against Out-of-distribution Data?cited by 14
  5. 2024Uncertainty-guided Contrastive Learning for Single Source Domain Generalisationcited by 14
  6. 2023Topology-aware Robust Optimization for Out-of-distribution Generalizationcited by 7
  7. 2022Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solutioncited by 6
  8. 2024SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Surveycited by 6
  9. 2025When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approachcited by 5
  10. 2024RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentationcited by 3
  11. 2024FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalizationcited by 3
  12. 2024Domain Expansion and Boundary Growth for Open-Set Single-Source Domain Generalizationcited by 2
6 more
  1. 2023Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic Segmentationcited by 1
  2. 2024Producing Plankton Classifiers that are Robust to Dataset Shiftcited by 1
  3. 2023HD Maps are Lane Detection Generalizers: A Novel Generative Framework for Single-Source Domain Generalizationcited by 0
  4. 2025MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality imagescited by 0
  5. 2026FOCUS: Bridging Fine-Grained Recognition and Open-World Discovery across Domainscited by 0
  6. 2026VFM4SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detectioncited by 0