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Deep Domain Confusion: Maximizing for Domain Invariance

arXiv 1412.3474 ↗First posted 10 Dec 2014cs.CVcited by 597

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Abstract (arXiv)

Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning deep models in a new domain can require a significant amount of data, which for many applications is simply not available. We propose a new CNN architecture which introduces an adaptation layer and an additional domain confusion loss, to learn a representation that is both semantically meaningful and domain invariant. We additionally show that a domain confusion metric can be used for model selection to determine the dimension of an adaptation layer and the best position for the layer in the CNN architecture. Our proposed adaptation method offers empirical performance which exceeds previously published results on a standard benchmark visual domain adaptation task.

Key facts. Cited by 597 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2014.

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Citing papers per year: 2018: 1; 2019: 2; 2020: 2; 2021: 157; 2022: 148; 2023: 115; 2024: 81; 2025: 58; 2026: 33.

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  1. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  2. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  3. 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentationcited by 259
  4. 2022Contrastive Test-Time Adaptationcited by 258
  5. 2022Surgical Fine-Tuning Improves Adaptation to Distribution Shiftscited by 205
  6. 2022Improving Out-of-Distribution Robustness via Selective Augmentationcited by 202
  7. 2021Fishr: Invariant Gradient Variances for Out-of-Distribution Generalizationcited by 186
  8. 2019Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Modelscited by 177
  9. 2021Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptationcited by 176
  10. 2021Generalized Source-free Domain Adaptationcited by 175
  11. 2021CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptationcited by 129
  12. 2022Domain Adaptation via Prompt Learningcited by 128
38 more
  1. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  2. 2021Causality-inspired Single-source Domain Generalization for Medical Image Segmentationcited by 118
  3. 2021Predicting with Confidence on Unseen Distributionscited by 116
  4. 2021Source-free Domain Adaptation via Avatar Prototype Generation and Adaptationcited by 111
  5. 2021AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptationcited by 104
  6. 2022Test-Time Adaptation via Conjugate Pseudo-labelscited by 98
  7. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  8. 2021Progressive Domain Expansion Network for Single Domain Generalizationcited by 96
  9. 2022Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptationcited by 93
  10. 2021Domain Impression: A Source Data Free Domain Adaptation Methodcited by 91
  11. 2022Understanding and Improving Visual Prompting: A Label-Mapping Perspectivecited by 86
  12. 2022Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Timecited by 85
  13. 2021Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptationcited by 83
  14. 2021TVT: Transferable Vision Transformer for Unsupervised Domain Adaptationcited by 81
  15. 2022OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mappingcited by 80
  16. 2021Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Testscited by 77
  17. 2021Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentationcited by 76
  18. 2023Sensitivity-Aware Visual Parameter-Efficient Fine-Tuningcited by 74
  19. 2019Third-Person Visual Imitation Learning via Decoupled Hierarchical Controllercited by 73
  20. 2020Fighting Copycat Agents in Behavioral Cloning from Observation Historiescited by 70
  21. 2022Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptationcited by 66
  22. 2022Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptationcited by 66
  23. 2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
  24. 2022A Broad Study of Pre-training for Domain Generalization and Adaptationcited by 64
  25. 2023Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptationcited by 63
  26. 2018Multi-Source Domain Adaptation with Mixture of Expertscited by 62
  27. 2021A Prototype-Oriented Framework for Unsupervised Domain Adaptationcited by 62
  28. 2022Safe Self-Refinement for Transformer-based Domain Adaptationcited by 61
  29. 2022Transferability in Deep Learning: A Surveycited by 57
  30. 2022C-Mixup: Improving Generalization in Regressioncited by 57
  31. 2021Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentationcited by 56
  32. 2021Causally motivated Shortcut Removal Using Auxiliary Labelscited by 55
  33. 2022Diversify and Disambiguate: Learning From Underspecified Datacited by 55
  34. 2021MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptationcited by 54
  35. 2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
  36. 2021Dynamic Weighted Learning for Unsupervised Domain Adaptationcited by 53
  37. 2022Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learningcited by 53
  38. 2023A Survey on Time-Series Pre-Trained Modelscited by 52

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