Data to 7 Oct 2026

A Survey of Unsupervised Deep Domain Adaptation

arXiv 1812.02849 ↗First posted 6 Dec 2018cs.LG stat.MLcited by 361

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Abstract (arXiv)

Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially-costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.

Key facts. Cited by 361 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 66; 2022: 79; 2023: 78; 2024: 76; 2025: 44; 2026: 18.

361 papers.

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2021MEMO: Test Time Robustness via Adaptation and Augmentationcited by 376
2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 353
2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
2022Parameter-free Online Test-time Adaptationcited by 184
2021CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptationcited by 129
2023DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Modelscited by 122
2021Measure and Improve Robustness in NLP Models: A Surveycited by 107
2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
2022Robust Mean Teacher for Continual and Gradual Test-Time Adaptationcited by 102
2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
2021Unsupervised Domain Adaptation of Object Detectors: A Surveycited by 93

38 more

2023Collaborative Perception in Autonomous Driving: Methods, Datasets and Challengescited by 87
2024Robust agents learn causal world modelscited by 81
2023Federated Learning for Medical Image Analysis: A Surveycited by 77
2021Domain Adaptation for Time Series Forecasting via Attention Sharingcited by 68
2022A Survey on Programmatic Weak Supervisioncited by 66
2021MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaptioncited by 65
2023Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correctioncited by 65
2022Safe Self-Refinement for Transformer-based Domain Adaptationcited by 61
2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
2021MixNorm: Test-Time Adaptation Through Online Normalization Estimationcited by 47
2021A Survey on Deep Domain Adaptation for LiDAR Perceptioncited by 46
2022A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Predictioncited by 46
2023Prompt-based Distribution Alignment for Unsupervised Domain Adaptationcited by 44
2021Nonlinear Invariant Risk Minimization: A Causal Approachcited by 42
2021Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directionscited by 41
2021ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenationcited by 41
2023All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenescited by 40
2021Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentationcited by 39
2021No-Reference Point Cloud Quality Assessment via Domain Adaptationcited by 37
2021Bridging the Gap between Events and Frames through Unsupervised Domain Adaptationcited by 36
2022SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networkscited by 36
2022Invariant Feature Learning for Generalized Long-Tailed Classificationcited by 34
2022Domain-invariant Feature Exploration for Domain Generalizationcited by 34
2021Geometric Unsupervised Domain Adaptation for Semantic Segmentationcited by 33
2022TTTFlow: Unsupervised Test-Time Training with Normalizing Flowcited by 33
2022A System-Level View on Out-of-Distribution Data in Roboticscited by 33
2022Deliberated Domain Bridging for Domain Adaptive Semantic Segmentationcited by 31
2022Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignmentcited by 31
2023RLSbench: Domain Adaptation Under Relaxed Label Shiftcited by 30
2023Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentationcited by 30
2023Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Drivingcited by 29
2023Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalizationcited by 29
2021Unsupervised Monocular Depth Estimation in Highly Complex Environmentscited by 28
2022Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognitioncited by 28
2023DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matricescited by 28
2021Confidence Calibration for Domain Generalization under Covariate Shiftcited by 27
2022I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervisioncited by 26
2023Semantic Image Segmentation: Two Decades of Researchcited by 26

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