mlsanityData to 7 Oct 2026

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

arXiv 1711.03213 ↗First posted 8 Nov 2017cs.CVcited by 1,079

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Cited by 21 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.

Key facts. Cited by 1,079 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Nov 2017.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 341; 2022: 264; 2023: 222; 2024: 128; 2025: 85; 2026: 38.

1,079 papers.

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  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2021Domain Generalization: A Surveycited by 760
  3. 2022Continual Test-Time Domain Adaptationcited by 520
  4. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 353
  5. 2021DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentationcited by 299
  6. 2023UniSim: A Neural Closed-Loop Sensor Simulatorcited by 294
  7. 2021A Fourier-based Framework for Domain Generalizationcited by 287
  8. 2022Exploring Visual Prompts for Adapting Large-Scale Modelscited by 271
  9. 2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
  10. 2022Contrastive Test-Time Adaptationcited by 258
  11. 2021SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retrainingcited by 235
  12. 2021RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whiteningcited by 213
38 more
  1. 2022Test-Time Training with Masked Autoencoderscited by 208
  2. 2022MIC: Masked Image Consistency for Context-Enhanced Domain Adaptationcited by 176
  3. 2021Source-Free Domain Adaptation for Semantic Segmentationcited by 168
  4. 2021FSDR: Frequency Space Domain Randomization for Domain Generalizationcited by 163
  5. 2022HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentationcited by 163
  6. 2021ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 158
  7. 2021Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Datacited by 152
  8. 2021Self-supervised Augmentation Consistency for Adapting Semantic Segmentationcited by 149
  9. 2022SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptationcited by 146
  10. 2021CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptationcited by 129
  11. 2021Cycle Self-Training for Domain Adaptationcited by 120
  12. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  13. 2021Learning to Track with Object Permanencecited by 117
  14. 2021Source-free Domain Adaptation via Avatar Prototype Generation and Adaptationcited by 111
  15. 2021MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detectioncited by 106
  16. 2021Regularizing Generative Adversarial Networks under Limited Datacited by 105
  17. 2021Generative Models as a Data Source for Multiview Representation Learningcited by 104
  18. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
  19. 2022Robust Mean Teacher for Continual and Gradual Test-Time Adaptationcited by 102
  20. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  21. 2022Visual Prompt Tuning for Test-time Domain Adaptationcited by 97
  22. 2021Unsupervised Domain Adaptation of Object Detectors: A Surveycited by 93
  23. 2021Domain Impression: A Source Data Free Domain Adaptation Methodcited by 91
  24. 2022Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoningcited by 90
  25. 2022SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentationcited by 87
  26. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 85
  27. 2021Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptationcited by 83
  28. 2021Breaking the Dilemma of Medical Image-to-image Translationcited by 82
  29. 2022Learning to generate line drawings that convey geometry and semanticscited by 82
  30. 2021TVT: Transferable Vision Transformer for Unsupervised Domain Adaptationcited by 81
  31. 2021Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentationcited by 81
  32. 2021Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimationcited by 80
  33. 2021PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Trainingcited by 80
  34. 2021Context-Aware Mixup for Domain Adaptive Semantic Segmentationcited by 79
  35. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  36. 2021Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigationcited by 78
  37. 2022Semantic-Aware Domain Generalized Segmentationcited by 78
  38. 2022Deep Clustering: A Comprehensive Surveycited by 78

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