mlsanityData to 7 Oct 2026

A Survey of Deep Active Learning

arXiv 2009.00236 ↗First posted 30 Aug 2020cs.LG stat.MLcited by 547

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Abstract (arXiv)

Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features. In recent years, due to the rapid development of internet technology, we are in an era of information torrents and we have massive amounts of data. In this way, DL has aroused strong interest of researchers and has been rapidly developed. Compared with DL, researchers have relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples. Therefore, early AL is difficult to reflect the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to the publicity of the large number of existing annotation datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, which is not allowed in some fields that require high expertise, especially in the fields of speech recognition, information extraction, medical images, etc. Therefore, AL has gradually received due attention. A natural idea is whether AL can be used to reduce the cost of sample annotations, while retaining the powerful learning capabilities of DL. Therefore, deep active learning (DAL) has emerged. Although the related research has been quite abundant, it lacks a comprehensive survey of DAL. This article is to fill this gap, we provide a formal classification method for the existing work, and a comprehensive and systematic overview. In addition, we also analyzed and summarized the development of DAL from the perspective of application. Finally, we discussed the confusion and problems in DAL, and gave some possible development directions for DAL.

Key facts. Cited by 547 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Aug 2020.

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Citing papers per year: 2021: 41; 2022: 83; 2023: 139; 2024: 127; 2025: 92; 2026: 65.

547 papers.

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  1. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  2. 2022Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisonscited by 169
  3. 2022Training Data Influence Analysis and Estimation: A Surveycited by 134
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  5. 2023Finding Support Examples for In-Context Learningcited by 104
  6. 2022A Comparative Survey of Deep Active Learningcited by 88
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  9. 2021Influence Selection for Active Learningcited by 82
  10. 2022Efficient Methods for Natural Language Processing: A Surveycited by 78
  11. 2023A Survey on Uncertainty Quantification Methods for Deep Learningcited by 78
  12. 2023LLMaAA: Making Large Language Models as Active Annotatorscited by 78
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  2. 2021CoNeRF: Controllable Neural Radiance Fieldscited by 65
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  4. 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Reviewcited by 57
  5. 2023TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPTcited by 57
  6. 2021Topology-Imbalance Learning for Semi-Supervised Node Classificationcited by 56
  7. 2025QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?cited by 56
  8. 2024Active Preference Learning for Large Language Modelscited by 55
  9. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 53
  10. 2025A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Futurecited by 53
  11. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  12. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  13. 2021SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistencycited by 47
  14. 2022A Framework and Benchmark for Deep Batch Active Learning for Regressioncited by 47
  15. 2024Active Statistical Inferencecited by 46
  16. 2023Data-centric AI: Perspectives and Challengescited by 43
  17. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  18. 2023Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigmcited by 36
  19. 2023Continuous Learning for Android Malware Detectioncited by 33
  20. 2024Active Evaluation Acquisition for Efficient LLM Benchmarkingcited by 33
  21. 2022Discovering and forecasting extreme events via active learning in neural operatorscited by 32
  22. 2023Topology-Aware Uncertainty for Image Segmentationcited by 32
  23. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  24. 2024Technical Report on the Pangram AI-Generated Text Classifiercited by 32
  25. 2023Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedbackcited by 31
  26. 2024Active Generalized Category Discoverycited by 31
  27. 2024A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Sciencecited by 31
  28. 2024PINNACLE: PINN Adaptive ColLocation and Experimental points selectioncited by 31
  29. 2021All you need are a few pixels: semantic segmentation with PixelPickcited by 30
  30. 2023Active learning for data streams: a surveycited by 29
  31. 2021Adaptive deep density approximation for Fokker-Planck equationscited by 28
  32. 2021ILabel: Interactive Neural Scene Labellingcited by 28
  33. 2023A Review of Deep Learning for Video Captioningcited by 28
  34. 2023Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forwardcited by 28
  35. 2021Data Shapley Valuation for Efficient Batch Active Learningcited by 27
  36. 2021Unsupervised Selective Labeling for More Effective Semi-Supervised Learningcited by 27
  37. 2025Language Model Personalization via Reward Factorizationcited by 27
  38. 2023Exploring Active 3D Object Detection from a Generalization Perspectivecited by 26

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