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Contrastive Adaptation Network for Unsupervised Domain Adaptation

arXiv 1901.00976 ↗First posted 4 Jan 2019cs.CVcited by 407

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Abstract (arXiv)

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. To address this issue, this paper proposes Contrastive Adaptation Network (CAN) optimizing a new metric which explicitly models the intra-class domain discrepancy and the inter-class domain discrepancy. We design an alternating update strategy for training CAN in an end-to-end manner. Experiments on two real-world benchmarks Office-31 and VisDA-2017 demonstrate that CAN performs favorably against the state-of-the-art methods and produces more discriminative features.

Key facts. Cited by 407 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jan 2019.

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Citing papers per year: 2021: 115; 2022: 81; 2023: 86; 2024: 66; 2025: 45; 2026: 14.

407 papers.

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  1. 2021Domain Generalization: A Surveycited by 760
  2. 2022Contrastive Test-Time Adaptationcited by 258
  3. 2022Parameter-free Online Test-time Adaptationcited by 184
  4. 2021FSDR: Frequency Space Domain Randomization for Domain Generalizationcited by 163
  5. 2021The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalizationcited by 131
  6. 2021CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptationcited by 129
  7. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  8. 2021Source-free Domain Adaptation via Avatar Prototype Generation and Adaptationcited by 111
  9. 2024Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factorscited by 106
  10. 2022Robust Mean Teacher for Continual and Gradual Test-Time Adaptationcited by 102
  11. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  12. 2022Source-Free Domain Adaptation via Distribution Estimationcited by 97
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  1. 2022Visual Prompt Tuning for Test-time Domain Adaptationcited by 97
  2. 2021Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representationscited by 96
  3. 2021Invariant Information Bottleneck for Domain Generalizationcited by 86
  4. 2022Debiased Learning from Naturally Imbalanced Pseudo-Labelscited by 85
  5. 2021Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptationcited by 83
  6. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 79
  7. 2022Deep Clustering: A Comprehensive Surveycited by 78
  8. 2021Cross-domain Contrastive Learning for Unsupervised Domain Adaptationcited by 75
  9. 2021VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptationcited by 69
  10. 2021Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networkscited by 68
  11. 2021Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptationcited by 67
  12. 2021Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptationcited by 67
  13. 2022Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptationcited by 66
  14. 2021Category Contrast for Unsupervised Domain Adaptation in Visual Taskscited by 65
  15. 2023Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correctioncited by 65
  16. 2023Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptationcited by 63
  17. 2021A Prototype-Oriented Framework for Unsupervised Domain Adaptationcited by 62
  18. 2023Domain Adaptation for Time Series Under Feature and Label Shiftscited by 60
  19. 2022Confidence Score for Source-Free Unsupervised Domain Adaptationcited by 58
  20. 2022Transferability in Deep Learning: A Surveycited by 57
  21. 2023Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detectioncited by 57
  22. 2021Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentationcited by 56
  23. 2023Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspectivecited by 54
  24. 2023Source-Free Domain Adaptation with Frozen Multimodal Foundation Modelcited by 54
  25. 2022Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learningcited by 53
  26. 2021CLDA: Contrastive Learning for Semi-Supervised Domain Adaptationcited by 52
  27. 2021DINE: Domain Adaptation from Single and Multiple Black-box Predictorscited by 51
  28. 2021Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuningcited by 50
  29. 2021Test-time Batch Statistics Calibration for Covariate Shiftcited by 50
  30. 2023C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptationcited by 49
  31. 2021RDA: Robust Domain Adaptation via Fourier Adversarial Attackingcited by 48
  32. 2021Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Datacited by 48
  33. 20233D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Cloudscited by 46
  34. 2021Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detectioncited by 45
  35. 2021Online Adaptation to Label Distribution Shiftcited by 44
  36. 2021Federated Multi-Target Domain Adaptationcited by 44
  37. 2021Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Densitycited by 44
  38. 2021Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directionscited by 41

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