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Multi-Adversarial Domain Adaptation

arXiv 1809.02176 ↗First posted 4 Sep 2018cs.CVcited by 195

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Abstract (arXiv)

Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains. Existing domain adversarial adaptation methods based on single domain discriminator only align the source and target data distributions without exploiting the complex multimode structures. In this paper, we present a multi-adversarial domain adaptation (MADA) approach, which captures multimode structures to enable fine-grained alignment of different data distributions based on multiple domain discriminators. The adaptation can be achieved by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Empirical evidence demonstrates that the proposed model outperforms state of the art methods on standard domain adaptation datasets.

Key facts. Cited by 195 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2018.

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Citing papers per year: 2021: 51; 2022: 41; 2023: 38; 2024: 31; 2025: 29; 2026: 5.

195 papers.

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  1. 2022Efficient Test-Time Model Adaptation without Forgettingcited by 467
  2. 2023Towards Stable Test-Time Adaptation in Dynamic Wild Worldcited by 409
  3. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  4. 2021Source-free Domain Adaptation via Avatar Prototype Generation and Adaptationcited by 111
  5. 2021Domain Impression: A Source Data Free Domain Adaptation Methodcited by 91
  6. 2021TVT: Transferable Vision Transformer for Unsupervised Domain Adaptationcited by 81
  7. 2022Deep Clustering: A Comprehensive Surveycited by 78
  8. 2021Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptationcited by 67
  9. 2021Adaptive Methods for Real-World Domain Generalizationcited by 66
  10. 2022Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptationcited by 66
  11. 2021MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptationcited by 54
  12. 2021CLDA: Contrastive Learning for Semi-Supervised Domain Adaptationcited by 52
38 more
  1. 2022Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Expertscited by 50
  2. 2021Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixingcited by 49
  3. 2021A Theory of Label Propagation for Subpopulation Shiftcited by 48
  4. 2021Your Classifier can Secretly Suffice Multi-Source Domain Adaptationcited by 42
  5. 2021Semantic Concentration for Domain Adaptationcited by 42
  6. 2021Transporting Causal Mechanisms for Unsupervised Domain Adaptationcited by 38
  7. 2022Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Citiescited by 38
  8. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  9. 2022Contrastive Learning for Unsupervised Domain Adaptation of Time Seriescited by 36
  10. 2025Model Adaptation: Unsupervised Domain Adaptation without Source Datacited by 34
  11. 2021ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptationcited by 33
  12. 2022Generative Domain Adaptation for Face Anti-Spoofingcited by 33
  13. 2022Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptationcited by 33
  14. 2023Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalizationcited by 31
  15. 2024Rethinking Propagation for Unsupervised Graph Domain Adaptationcited by 31
  16. 2021PIT: Position-Invariant Transform for Cross-FoV Domain Adaptationcited by 30
  17. 2021I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectorscited by 27
  18. 2022Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domaincited by 27
  19. 2022Gradual Domain Adaptation without Indexed Intermediate Domainscited by 26
  20. 2021Aggregating From Multiple Target-Shifted Sourcescited by 24
  21. 2021Generalized Domain Conditioned Adaptation Networkcited by 23
  22. 2023Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptationcited by 23
  23. 2021Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptationcited by 21
  24. 2024Uncertainty-Calibrated Test-Time Model Adaptation without Forgettingcited by 20
  25. 2022Multi-Representation Adaptation Network for Cross-domain Image Classificationcited by 19
  26. 2023DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentationcited by 19
  27. 2023GeoNet: Benchmarking Unsupervised Adaptation across Geographiescited by 17
  28. 2021Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehiclescited by 16
  29. 2022Weakly Supervised Object Localization as Domain Adaptioncited by 16
  30. 2022Unsupervised Structure-Texture Separation Network for Oracle Character Recognitioncited by 15
  31. 2022MemSAC: Memory Augmented Sample Consistency for Large Scale Unsupervised Domain Adaptationcited by 15
  32. 2022Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19cited by 15
  33. 2023Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioningcited by 14
  34. 2025Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptationcited by 14
  35. 2023Unsupervised Domain Adaption with Pixel-level Discriminator for Image-aware Layout Generationcited by 13
  36. 2024Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streamscited by 13
  37. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  38. 2024Unsupervised Domain Adaption Harnessing Vision-Language Pre-trainingcited by 12

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