mlsanityData to 14 Oct 2026

Deeper, Broader and Artier Domain Generalization

arXiv 1710.03077 ↗First posted 9 Oct 2017cs.CVcited by 986

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Cited by 38 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The problem of domain generalization is to learn from multiple training domains, and extract a domain-agnostic model that can then be applied to an unseen domain. Domain generalization (DG) has a clear motivation in contexts where there are target domains with distinct characteristics, yet sparse data for training. For example recognition in sketch images, which are distinctly more abstract and rarer than photos. Nevertheless, DG methods have primarily been evaluated on photo-only benchmarks focusing on alleviating the dataset bias where both problems of domain distinctiveness and data sparsity can be minimal. We argue that these benchmarks are overly straightforward, and show that simple deep learning baselines perform surprisingly well on them. In this paper, we make two main contributions: Firstly, we build upon the favorable domain shift-robust properties of deep learning methods, and develop a low-rank parameterized CNN model for end-to-end DG learning. Secondly, we develop a DG benchmark dataset covering photo, sketch, cartoon and painting domains. This is both more practically relevant, and harder (bigger domain shift) than existing benchmarks. The results show that our method outperforms existing DG alternatives, and our dataset provides a more significant DG challenge to drive future research.

Key facts. Cited by 986 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Oct 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 154; 2022: 185; 2023: 219; 2024: 178; 2025: 166; 2026: 84.

986 papers.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  2. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 766
  3. 2021Domain Generalization: A Surveycited by 764
  4. 2021Domain Generalization with MixStylecited by 566
  5. 2021Intriguing Properties of Vision Transformerscited by 446
  6. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  7. 2021SWAD: Domain Generalization by Seeking Flat Minimacited by 434
  8. 2021Deep Long-Tailed Learning: A Surveycited by 417
  9. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 391
  10. 2021A Fourier-based Framework for Domain Generalizationcited by 290
  11. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  12. 2021Gradient Matching for Domain Generalizationcited by 258
38 more
  1. 2021SelfReg: Self-supervised Contrastive Regularization for Domain Generalizationcited by 189
  2. 2021Fishr: Invariant Gradient Variances for Out-of-Distribution Generalizationcited by 186
  3. 2021A Fine-Grained Analysis on Distribution Shiftcited by 171
  4. 2021Learning to Diversify for Single Domain Generalizationcited by 170
  5. 2021FSDR: Frequency Space Domain Randomization for Domain Generalizationcited by 164
  6. 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
  7. 2022Diverse Weight Averaging for Out-of-Distribution Generalizationcited by 148
  8. 2022SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptationcited by 146
  9. 2021Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalizationcited by 144
  10. 2022Causality Inspired Representation Learning for Domain Generalizationcited by 138
  11. 2022Domain Generalization by Mutual-Information Regularization with Pre-trained Modelscited by 133
  12. 2025REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewardscited by 130
  13. 2022Uncertainty Modeling for Out-of-Distribution Generalizationcited by 125
  14. 2022Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalizationcited by 122
  15. 2021Instance-Conditioned GANcited by 121
  16. 2021Assessing Generalization of SGD via Disagreementcited by 117
  17. 2021Open Domain Generalization with Domain-Augmented Meta-Learningcited by 115
  18. 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictorscited by 113
  19. 2021Model-Based Domain Generalizationcited by 104
  20. 2021AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptationcited by 104
  21. 2023Sharpness-Aware Gradient Matching for Domain Generalizationcited by 102
  22. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  23. 2022Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalizationcited by 97
  24. 2023CLIPood: Generalizing CLIP to Out-of-Distributionscited by 96
  25. 2021Towards a Theoretical Framework of Out-of-Distribution Generalizationcited by 94
  26. 2021Towards Principled Disentanglement for Domain Generalizationcited by 91
  27. 2022Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Timecited by 90
  28. 2021OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalizationcited by 86
  29. 2021Invariant Information Bottleneck for Domain Generalizationcited by 86
  30. 2021Exploiting Domain-Specific Features to Enhance Domain Generalizationcited by 86
  31. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 86
  32. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
  33. 2021Adversarially Adaptive Normalization for Single Domain Generalizationcited by 85
  34. 2022Sparse Mixture-of-Experts are Domain Generalizable Learnerscited by 85
  35. 2021Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalizationcited by 83
  36. 2021Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentationcited by 81
  37. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 80
  38. 2023Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Taskscited by 72

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