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A Survey on Deep Transfer Learning

arXiv 1808.01974 ↗First posted 6 Aug 2018cs.LG stat.MLcited by 417

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Abstract (arXiv)

As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.

Key facts. Cited by 417 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Aug 2018.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 122; 2022: 100; 2023: 83; 2024: 55; 2025: 39; 2026: 16.

417 papers.

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  1. 2021LiT: Zero-Shot Transfer with Locked-image text Tuningcited by 572
  2. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  3. 2021Deep Long-Tailed Learning: A Surveycited by 416
  4. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
  5. 2021Scaling Laws for Transfercited by 265
  6. 2021A Survey on Deep Semi-supervised Learningcited by 250
  7. 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
  8. 2020Language Conditioned Imitation Learning over Unstructured Datacited by 208
  9. 2022FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learningcited by 120
  10. 2021AutoNovel: Automatically Discovering and Learning Novel Visual Categoriescited by 116
  11. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  12. 2021Self-Supervised Pretraining Improves Self-Supervised Pretrainingcited by 81
38 more
  1. 2021Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentationcited by 76
  2. 2022Visual Prompt Tuning for Generative Transfer Learningcited by 75
  3. 2023Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matricescited by 72
  4. 2021MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaptioncited by 65
  5. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  6. 2022A Review of Deep Transfer Learning and Recent Advancementscited by 56
  7. 2021DORO: Distributional and Outlier Robust Optimizationcited by 54
  8. 2021Improved Regularization and Robustness for Fine-tuning in Neural Networkscited by 45
  9. 2021Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directionscited by 41
  10. 2023When Prompt-based Incremental Learning Does Not Meet Strong Pretrainingcited by 41
  11. 2021Scalable Diverse Model Selection for Accessible Transfer Learningcited by 40
  12. 2022Cross-Architecture Knowledge Distillationcited by 40
  13. 2023Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Trainingcited by 39
  14. 2024A Survey of Camouflaged Object Detection and Beyondcited by 37
  15. 2021Neural Transfer Learning for Repairing Security Vulnerabilities in C Codecited by 36
  16. 2022Augmenting Interpretable Models with LLMs during Trainingcited by 36
  17. 2021Zoo-Tuning: Adaptive Transfer from a Zoo of Modelscited by 35
  18. 2021Rethinking Supervised Pre-training for Better Downstream Transferringcited by 35
  19. 2023Visual Tuningcited by 34
  20. 2021A Review on Deep Learning in UAV Remote Sensingcited by 32
  21. 2021SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Datacited by 31
  22. 2021The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactionscited by 31
  23. 2022Minimax Regret Optimization for Robust Machine Learning under Distribution Shiftcited by 31
  24. 2022Disentangling Transfer in Continual Reinforcement Learningcited by 31
  25. 2022Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Spacecited by 30
  26. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  27. 2021A Survey on Anomaly Detection for Technical Systems using LSTM Networkscited by 27
  28. 2023Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learningcited by 26
  29. 2021A Survey on Open Set Recognitioncited by 25
  30. 2023A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRUcited by 25
  31. 2019Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Datacited by 24
  32. 2021UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Modelscited by 23
  33. 2021Solving the electronic Schr"odinger equation for multiple nuclear geometries with weight-sharing deep neural networkscited by 22
  34. 2023For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equalcited by 22
  35. 2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
  36. 2021Phase Transitions in Transfer Learning for High-Dimensional Perceptronscited by 21
  37. 2021Cross-domain Activity Recognition via Substructural Optimal Transportcited by 21
  38. 2021Representation Learning Beyond Linear Prediction Functionscited by 21

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