mlsanityData to 7 Oct 2026

Generate To Adapt: Aligning Domains using Generative Adversarial Networks

arXiv 1704.01705 ↗First posted 6 Apr 2017cs.CVcited by 183

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Abstract (arXiv)

Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.

Key facts. Cited by 183 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2017.

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Citing papers per year: 2020: 1; 2021: 73; 2022: 46; 2023: 35; 2024: 16; 2025: 11; 2026: 1.

183 papers.

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  1. 2021Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Datacited by 152
  2. 2021Cross-Domain Adaptive Teacher for Object Detectioncited by 125
  3. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  4. 2021Source-free Domain Adaptation via Avatar Prototype Generation and Adaptationcited by 111
  5. 2021MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detectioncited by 106
  6. 2022Robust Mean Teacher for Continual and Gradual Test-Time Adaptationcited by 102
  7. 2023Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentationcited by 100
  8. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  9. 2021Unsupervised Domain Adaptation of Object Detectors: A Surveycited by 93
  10. 2021Domain Impression: A Source Data Free Domain Adaptation Methodcited by 91
  11. 2021Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptationcited by 83
  12. 2022Deep Clustering: A Comprehensive Surveycited by 78
38 more
  1. 2021Category Contrast for Unsupervised Domain Adaptation in Visual Taskscited by 65
  2. 2023Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correctioncited by 65
  3. 2022Transferability in Deep Learning: A Surveycited by 57
  4. 2021Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Typescited by 54
  5. 2021MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptationcited by 54
  6. 2021Dynamic Weighted Learning for Unsupervised Domain Adaptationcited by 53
  7. 2021RDA: Robust Domain Adaptation via Fourier Adversarial Attackingcited by 48
  8. 2021f-Domain-Adversarial Learning: Theory and Algorithmscited by 47
  9. 2021Source-Free Domain Adaptation for Image Segmentationcited by 45
  10. 2021Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iteratecited by 43
  11. 2021Your Classifier can Secretly Suffice Multi-Source Domain Adaptationcited by 42
  12. 2021Spectral Unsupervised Domain Adaptation for Visual Recognitioncited by 40
  13. 2021Transporting Causal Mechanisms for Unsupervised Domain Adaptationcited by 38
  14. 2021Cross-category Video Highlight Detection via Set-based Learningcited by 38
  15. 2022PASTA: Proportional Amplitude Spectrum Training Augmentation for Syn-to-Real Domain Generalizationcited by 38
  16. 2021Curriculum Graph Co-Teaching for Multi-Target Domain Adaptationcited by 37
  17. 2022Using Language to Extend to Unseen Domainscited by 34
  18. 2025Model Adaptation: Unsupervised Domain Adaptation without Source Datacited by 34
  19. 2021ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptationcited by 33
  20. 2021Unsupervised Domain Generalization by Learning a Bridge Across Domainscited by 33
  21. 2022Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptationcited by 33
  22. 2021Visualizing Adapted Knowledge in Domain Transfercited by 32
  23. 2021A Style and Semantic Memory Mechanism for Domain Generalizationcited by 32
  24. 2023A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithmcited by 32
  25. 2024Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptationcited by 32
  26. 2021Transformer-Based Source-Free Domain Adaptationcited by 30
  27. 2021PIT: Position-Invariant Transform for Cross-FoV Domain Adaptationcited by 30
  28. 2022TeST: Test-time Self-Training under Distribution Shiftcited by 29
  29. 2021Leveraging Self-Supervision for Cross-Domain Crowd Countingcited by 24
  30. 2021Aggregating From Multiple Target-Shifted Sourcescited by 24
  31. 2021Unsupervised Domain Adaptation: A Reality Checkcited by 24
  32. 2024Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalizationcited by 24
  33. 2021Generalized Domain Conditioned Adaptation Networkcited by 23
  34. 2021Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptationcited by 21
  35. 2020Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitationscited by 20
  36. 2022FHIST: A Benchmark for Few-shot Classification of Histological Imagescited by 20
  37. 2021Task Aligned Generative Meta-learning for Zero-shot Learningcited by 19
  38. 2021Learning Transferable Parameters for Unsupervised Domain Adaptationcited by 19

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