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Domain-Adversarial Neural Networks

arXiv 1412.4446 ↗First posted 15 Dec 2014stat.ML cs.LG cs.NEcited by 78

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Abstract (arXiv)

We introduce a new representation learning algorithm suited to the context of domain adaptation, in which data at training and test time come from similar but different distributions. Our algorithm is directly inspired by theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on a data representation that cannot discriminate between the training (source) and test (target) domains. We propose a training objective that implements this idea in the context of a neural network, whose hidden layer is trained to be predictive of the classification task, but uninformative as to the domain of the input. Our experiments on a sentiment analysis classification benchmark, where the target domain data available at training time is unlabeled, show that our neural network for domain adaption algorithm has better performance than either a standard neural network or an SVM, even if trained on input features extracted with the state-of-the-art marginalized stacked denoising autoencoders of Chen et al. (2012).

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Dec 2014.

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Citing papers per year: 2021: 17; 2022: 18; 2023: 12; 2024: 16; 2025: 10; 2026: 5.

78 papers.

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  1. 2022Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questionscited by 116
  2. 2021AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptationcited by 104
  3. 2025Reward Shaping to Mitigate Reward Hacking in RLHFcited by 88
  4. 2022Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networkscited by 73
  5. 2023Benchmarking Distribution Shift in Tabular Data with TableShiftcited by 64
  6. 2021Domain Invariant Representation Learning with Domain Density Transformationscited by 53
  7. 2021A Theory of Label Propagation for Subpopulation Shiftcited by 48
  8. 2021KL Guided Domain Adaptationcited by 35
  9. 2022Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyondcited by 32
  10. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  11. 2023Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalizationcited by 29
  12. 2022Domain Generalization by Learning and Removing Domain-specific Featurescited by 28
38 more
  1. 2022Causal Transportability for Visual Recognitioncited by 23
  2. 2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
  3. 2021Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistencycited by 19
  4. 2021Domain Adversarial Reinforcement Learningcited by 19
  5. 2023Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Groundingcited by 19
  6. 2023Gradual Domain Adaptation: Theory and Algorithmscited by 18
  7. 2024Active Test-Time Adaptation: Theoretical Analyses and An Algorithmcited by 18
  8. 2022Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methodscited by 17
  9. 2022Bootstrap Generalization Ability from Loss Landscape Perspectivecited by 16
  10. 2022Combating Label Distribution Shift for Active Domain Adaptationcited by 14
  11. 2022On The Effects Of Data Normalisation For Domain Adaptation On EEG Datacited by 13
  12. 2024CDAD-Net: Bridging Domain Gaps in Generalized Category Discoverycited by 12
  13. 2024Do causal predictors generalize better to new domains?cited by 11
  14. 2022StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessmentcited by 9
  15. 2024A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challengescited by 9
  16. 2021Learning Disentangled Representations for Time Seriescited by 8
  17. 2021Towards Evaluating the Robustness of Neural Networks Learned by Transductioncited by 8
  18. 2021Failure Modes of Domain Generalization Algorithmscited by 8
  19. 2023SATA: Source Anchoring and Target Alignment Network for Continual Test Time Adaptationcited by 8
  20. 2022BP-Triplet Net for Unsupervised Domain Adaptation: A Bayesian Perspectivecited by 7
  21. 2021Spatial Graph Convolutional Neural Network via Structured Subdomain Adaptation and Domain Adversarial Learning for Bearing Fault Diagnosiscited by 6
  22. 2022PAC-Net: A Model Pruning Approach to Inductive Transfer Learningcited by 6
  23. 2022Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalizationcited by 6
  24. 2024Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognitioncited by 6
  25. 2022Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gapcited by 5
  26. 2022Adversarial Support Alignmentcited by 5
  27. 2024DAdEE: Unsupervised Domain Adaptation in Early Exit PLMscited by 5
  28. 2025Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scenecited by 5
  29. 2025When Shift Happens - Confounding Is to Blamecited by 5
  30. 2023Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detectioncited by 4
  31. 2024Cross Domain Early Crop Mapping using CropSTGANcited by 4
  32. 2021Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shiftcited by 3
  33. 2021FRIDA -- Generative Feature Replay for Incremental Domain Adaptationcited by 3
  34. 2023Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognitioncited by 3
  35. 2023Target-agnostic Source-free Domain Adaptation for Regression Taskscited by 3
  36. 2024Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptationcited by 3
  37. 2026ALERT: Zero-shot LLM Jailbreak Detection via Internal Discrepancy Amplificationcited by 3
  38. 2021Learning Purified Feature Representations from Task-irrelevant Labelscited by 2

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