mlsanityData to 7 Oct 2026

Unified Deep Supervised Domain Adaptation and Generalization

arXiv 1709.10190 ↗First posted 28 Sep 2017cs.CVcited by 322

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Abstract (arXiv)

This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and where mapped visual domains are semantically aligned and yet maximally separated. The supervised setting becomes attractive especially when only few target data samples need to be labeled. In this scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by reverting to point-wise surrogates of distribution distances and similarities provides an effective solution. In addition, the approach has a high speed of adaptation, which requires an extremely low number of labeled target training samples, even one per category can be effective. The approach is extended to domain generalization. For both applications the experiments show very promising results.

Key facts. Cited by 322 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Sep 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 80; 2022: 73; 2023: 78; 2024: 42; 2025: 35; 2026: 14.

322 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. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  5. 2021A Fourier-based Framework for Domain Generalizationcited by 287
  6. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  7. 2021Gradient Matching for Domain Generalizationcited by 258
  8. 2021RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whiteningcited by 213
  9. 2021SelfReg: Self-supervised Contrastive Regularization for Domain Generalizationcited by 189
  10. 2021Learning to Diversify for Single Domain Generalizationcited by 170
  11. 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
  12. 2022Causality Inspired Representation Learning for Domain Generalizationcited by 136
38 more
  1. 2021MSeg: A Composite Dataset for Multi-domain Semantic Segmentationcited by 122
  2. 2022Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalizationcited by 122
  3. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
  4. 2021Measure and Improve Robustness in NLP Models: A Surveycited by 107
  5. 2021Model-Based Domain Generalizationcited by 103
  6. 2021Progressive Domain Expansion Network for Single Domain Generalizationcited by 96
  7. 2021Adversarially Adaptive Normalization for Single Domain Generalizationcited by 85
  8. 2022Semantic-Aware Domain Generalized Segmentationcited by 78
  9. 2021Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?cited by 75
  10. 2022Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentationcited by 69
  11. 2021MixStyle Neural Networks for Domain Generalization and Adaptationcited by 64
  12. 2022Test-Time Adaptation via Self-Training with Nearest Neighbor Informationcited by 64
  13. 2022Prompt Vision Transformer for Domain Generalizationcited by 52
  14. 2022Pin the Memory: Learning to Generalize Semantic Segmentationcited by 48
  15. 2021Style Normalization and Restitution for Domain Generalization and Adaptationcited by 46
  16. 2021Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detectioncited by 45
  17. 2021Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalizationcited by 45
  18. 2023Meta-causal Learning for Single Domain Generalizationcited by 43
  19. 2021Nonlinear Invariant Risk Minimization: A Causal Approachcited by 42
  20. 2023Feature Alignment and Uniformity for Test Time Adaptationcited by 42
  21. 2021Uncertainty-guided Model Generalization to Unseen Domainscited by 41
  22. 2022FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Drivingcited by 41
  23. 2022Deep Frequency Filtering for Domain Generalizationcited by 41
  24. 2022PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentationcited by 41
  25. 2021Towards Unsupervised Domain Generalizationcited by 40
  26. 2021Feature Stylization and Domain-aware Contrastive Learning for Domain Generalizationcited by 40
  27. 2022Compound Domain Generalization via Meta-Knowledge Encodingcited by 40
  28. 2022Robust and Efficient Medical Imaging with Self-Supervisioncited by 40
  29. 2023A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidancecited by 40
  30. 2021Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domainscited by 39
  31. 2022Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofingcited by 38
  32. 2022Attention Consistency on Visual Corruptions for Single-Source Domain Generalizationcited by 37
  33. 2021Domain Generalization via Inference-time Label-Preserving Target Projectionscited by 35
  34. 2022Self-Distilled Vision Transformer for Domain Generalizationcited by 35
  35. 2022Learning Domain Invariant Prompt for Vision-Language Modelscited by 35
  36. 2022Domain-invariant Feature Exploration for Domain Generalizationcited by 34
  37. 2021Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentationcited by 33
  38. 2021LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentationcited by 33

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