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Deep Transfer Learning with Joint Adaptation Networks

arXiv 1605.06636 ↗First posted 21 May 2016cs.LG stat.MLcited by 612

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Abstract (arXiv)

Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion. Adversarial training strategy is adopted to maximize JMMD such that the distributions of the source and target domains are made more distinguishable. Learning can be performed by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Experiments testify that our model yields state of the art results on standard datasets.

Key facts. Cited by 612 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 May 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 174; 2022: 145; 2023: 113; 2024: 95; 2025: 60; 2026: 25.

612 papers.

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  1. 2022MIC: Masked Image Consistency for Context-Enhanced Domain Adaptationcited by 178
  2. 2021A Fine-Grained Analysis on Distribution Shiftcited by 171
  3. 2021Learning to Diversify for Single Domain Generalizationcited by 170
  4. 2021Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Datacited by 152
  5. 2021CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptationcited by 129
  6. 2022Domain Adaptation via Prompt Learningcited by 128
  7. 2021Cross-Domain Adaptive Teacher for Object Detectioncited by 126
  8. 2021Cycle Self-Training for Domain Adaptationcited by 120
  9. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  10. 2021Predicting with Confidence on Unseen Distributionscited by 116
  11. 2021MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detectioncited by 106
  12. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
38 more
  1. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  2. 2022Source-Free Domain Adaptation via Distribution Estimationcited by 97
  3. 2021Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Datacited by 87
  4. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 86
  5. 2021Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptationcited by 83
  6. 2021TVT: Transferable Vision Transformer for Unsupervised Domain Adaptationcited by 81
  7. 2022Deep Clustering: A Comprehensive Surveycited by 78
  8. 2021Cross-domain Contrastive Learning for Unsupervised Domain Adaptationcited by 76
  9. 2023DELTA: degradation-free fully test-time adaptationcited by 72
  10. 2021VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptationcited by 69
  11. 2021Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptationcited by 67
  12. 2022Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptationcited by 66
  13. 2021Category Contrast for Unsupervised Domain Adaptation in Visual Taskscited by 65
  14. 2021Playing for 3D Human Recoverycited by 64
  15. 2021A Prototype-Oriented Framework for Unsupervised Domain Adaptationcited by 62
  16. 2023AD-CLIP: Adapting Domains in Prompt Space Using CLIPcited by 62
  17. 2022Safe Self-Refinement for Transformer-based Domain Adaptationcited by 61
  18. 2022Balancing Discriminability and Transferability for Source-Free Domain Adaptationcited by 58
  19. 2022Transferability in Deep Learning: A Surveycited by 57
  20. 2021Transferable Semantic Augmentation for Domain Adaptationcited by 56
  21. 2021MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptationcited by 54
  22. 2023Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspectivecited by 54
  23. 2021Dynamic Weighted Learning for Unsupervised Domain Adaptationcited by 53
  24. 2021Active Learning for Domain Adaptation: An Energy-Based Approachcited by 53
  25. 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
  26. 2021DINE: Domain Adaptation from Single and Multiple Black-box Predictorscited by 51
  27. 2021IDM: An Intermediate Domain Module for Domain Adaptive Person Re-IDcited by 51
  28. 2021Optimal Representations for Covariate Shiftcited by 51
  29. 2021Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixingcited by 49
  30. 2021A Theory of Label Propagation for Subpopulation Shiftcited by 48
  31. 2021RDA: Robust Domain Adaptation via Fourier Adversarial Attackingcited by 48
  32. 2021f-Domain-Adversarial Learning: Theory and Algorithmscited by 47
  33. 2021Style Normalization and Restitution for Domain Generalization and Adaptationcited by 46
  34. 2022Uncertainty-guided Source-free Domain Adaptationcited by 46
  35. 2021Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detectioncited by 45
  36. 2021Federated Multi-Target Domain Adaptationcited by 44
  37. 2022Transfer without Forgettingcited by 44
  38. 2023Prompt-based Distribution Alignment for Unsupervised Domain Adaptationcited by 44

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