mlsanityData to 7 Oct 2026

Active Adversarial Domain Adaptation

arXiv 1904.07848 ↗First posted 16 Apr 2019cs.CV cs.LGcited by 73

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Abstract (arXiv)

We propose an active learning approach for transferring representations across domains. Our approach, active adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain alignment and importance sampling for adapting models across domains. The former uses a domain discriminative model to align domains, while the latter utilizes it to weigh samples to account for distribution shifts. Specifically, our importance weight promotes samples with large uncertainty in classification and diversity from labeled examples, thus serves as a sample selection scheme for active learning. We show that these two views can be unified in one framework for domain adaptation and transfer learning when the source domain has many labeled examples while the target domain does not. AADA provides significant improvements over fine-tuning based approaches and other sampling methods when the two domains are closely related. Results on challenging domain adaptation tasks, e.g., object detection, demonstrate that the advantage over baseline approaches is retained even after hundreds of examples being actively annotated.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2019.

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Citing papers per year: 2021: 14; 2022: 13; 2023: 22; 2024: 16; 2025: 6; 2026: 2.

73 papers.

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  1. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
  2. 2021Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentationcited by 56
  3. 2021Dynamic Weighted Learning for Unsupervised Domain Adaptationcited by 53
  4. 2021Active Learning for Domain Adaptation: An Energy-Based Approachcited by 53
  5. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 53
  6. 2021LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentationcited by 33
  7. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  8. 2021Multi-Anchor Active Domain Adaptation for Semantic Segmentationcited by 29
  9. 2023Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitalscited by 25
  10. 2023Dirichlet-based Uncertainty Calibration for Active Domain Adaptationcited by 23
  11. 2023Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detectioncited by 21
  12. 2021S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptationcited by 19
38 more
  1. 2022Learning Distinctive Margin toward Active Domain Adaptationcited by 18
  2. 2023Hyperbolic Active Learning for Semantic Segmentation under Domain Shiftcited by 18
  3. 2024Active Test-Time Adaptation: Theoretical Analyses and An Algorithmcited by 18
  4. 2021Discrepancy-Based Active Learning for Domain Adaptationcited by 16
  5. 2024A Short Survey on Importance Weighting for Machine Learningcited by 15
  6. 2022Combating Label Distribution Shift for Active Domain Adaptationcited by 14
  7. 2023Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Surveycited by 14
  8. 2023Evidential Uncertainty Quantification: A Variance-Based Perspectivecited by 14
  9. 2023Divide and Adapt: Active Domain Adaptation via Customized Learningcited by 13
  10. 2022Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Surveycited by 12
  11. 2023Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentationcited by 11
  12. 2024Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Datasetcited by 11
  13. 2021A Comparative Review of Recent Few-Shot Object Detection Algorithmscited by 10
  14. 2022Active Source Free Domain Adaptationcited by 10
  15. 2022SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detectioncited by 10
  16. 2023Towards Balanced Active Learning for Multimodal Classificationcited by 9
  17. 2024Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptationcited by 9
  18. 2021Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentationcited by 8
  19. 2021Online Continual Adaptation with Active Self-Trainingcited by 8
  20. 2021Bayesian Active Learning for Sim-to-Real Robotic Perceptioncited by 8
  21. 2022Placing Human Animations into 3D Scenes by Learning Interaction- and Geometry-Driven Keyframescited by 8
  22. 2024Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistencecited by 8
  23. 2023Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic Segmentationcited by 7
  24. 2023Generalized Universal Domain Adaptation with Generative Flow Networkscited by 7
  25. 2021Multi-Domain Active Learning: Literature Review and Comparative Studycited by 6
  26. 2024Out-of-Distribution Learning with Human Feedbackcited by 6
  27. 2022D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentationcited by 5
  28. 2023MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentationcited by 5
  29. 2025LogAction: Consistent Cross-system Anomaly Detection through Logs via Active Domain Adaptationcited by 5
  30. 2022Local Context-Aware Active Domain Adaptationcited by 4
  31. 2022Cost-effective Framework for Gradual Domain Adaptation with Multifidelitycited by 3
  32. 2023ASPEST: Bridging the Gap Between Active Learning and Selective Predictioncited by 3
  33. 2023Spectral Transfer Guided Active Domain Adaptation For Thermal Imagerycited by 3
  34. 2024Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guaranteescited by 3
  35. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  36. 2025Certainty and Uncertainty Guided Active Domain Adaptationcited by 3
  37. 2022Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selectioncited by 2
  38. 2023Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learningcited by 2

50 most cited of 73 All 73 (JSON)