mlsanityData to 7 Oct 2026

Discriminative Active Learning

arXiv 1907.06347 ↗First posted 15 Jul 2019cs.LG stat.MLcited by 97

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Abstract (arXiv)

We propose a new batch mode active learning algorithm designed for neural networks and large query batch sizes. The method, Discriminative Active Learning (DAL), poses active learning as a binary classification task, attempting to choose examples to label in such a way as to make the labeled set and the unlabeled pool indistinguishable. Experimenting on image classification tasks, we empirically show our method to be on par with state of the art methods in medium and large query batch sizes, while being simple to implement and also extend to other domains besides classification tasks. Our experiments also show that none of the state of the art methods of today are clearly better than uncertainty sampling when the batch size is relatively large, negating some of the reported results in the recent literature.

Key facts. Cited by 97 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jul 2019.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 17; 2022: 27; 2023: 25; 2024: 19; 2025: 4; 2026: 4.

97 papers.

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  1. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  2. 2021Active Learning by Acquiring Contrastive Examplescited by 154
  3. 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 131
  4. 2021Gone Fishing: Neural Active Learning with Fisher Embeddingscited by 88
  5. 2022A Comparative Survey of Deep Active Learningcited by 88
  6. 2022Efficient Methods for Natural Language Processing: A Surveycited by 78
  7. 2022Active Learning Through a Covering Lenscited by 62
  8. 2021Active Learning for Domain Adaptation: An Energy-Based Approachcited by 53
  9. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  10. 2022Uncertainty Estimation for Language Reward Modelscited by 35
  11. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  12. 2022GALAXY: Graph-based Active Learning at the Extremecited by 29
38 more
  1. 2021Cartography Active Learningcited by 26
  2. 2023The Efficiency Spectrum of Large Language Models: An Algorithmic Surveycited by 25
  3. 2022A Lagrangian Duality Approach to Active Learningcited by 24
  4. 2022ALLSH: Active Learning Guided by Local Sensitivity and Hardnesscited by 23
  5. 2023Multi-Resolution Active Learning of Fourier Neural Operatorscited by 22
  6. 2022Batch Multi-Fidelity Active Learning with Budget Constraintscited by 20
  7. 2023Streaming Active Learning with Deep Neural Networkscited by 20
  8. 2021Small-Text: Active Learning for Text Classification in Pythoncited by 18
  9. 2023Algorithm Selection for Deep Active Learning with Imbalanced Datasetscited by 18
  10. 2022Active Learning Strategies for Weakly-supervised Object Detectioncited by 17
  11. 2023ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variationcited by 17
  12. 2021Discrepancy-Based Active Learning for Domain Adaptationcited by 16
  13. 2023How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budgetcited by 16
  14. 2021Multi-class Text Classification using BERT-based Active Learningcited by 11
  15. 2021Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Reviewcited by 11
  16. 2022Active Learning Over Multiple Domains in Natural Language Taskscited by 11
  17. 2022TiDAL: Learning Training Dynamics for Active Learningcited by 11
  18. 2023Gaussian Switch Sampling: A Second Order Approach to Active Learningcited by 10
  19. 2021Hitting the Target: Stopping Active Learning at the Cost-Based Optimumcited by 9
  20. 2022Patient Aware Active Learning for Fine-Grained OCT Classificationcited by 9
  21. 2023On the Limitations of Simulating Active Learningcited by 9
  22. 2023Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Datacited by 8
  23. 2021A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexitycited by 7
  24. 2022Deep Active Learning for Computer Vision: Past and Futurecited by 7
  25. 2023Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Modelscited by 7
  26. 2023On the Opportunities of Green Computing: A Surveycited by 7
  27. 2024Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learningcited by 7
  28. 2024AHA: Human-Assisted Out-of-Distribution Generalization and Detectioncited by 7
  29. 2021Multi-Domain Active Learning: Literature Review and Comparative Studycited by 6
  30. 2022Neural Design for Genetic Perturbation Experimentscited by 6
  31. 2023Investigating Multi-source Active Learning for Natural Language Inferencecited by 6
  32. 2021Active learning for object detection in high-resolution satellite imagescited by 5
  33. 2021Active learning for imbalanced data under cold startcited by 5
  34. 2022"Diversity and Uncertainty in Moderation" are the Key to Data Selection for Multilingual Few-shot Transfercited by 5
  35. 2022Neural Active Learning on Heteroskedastic Distributionscited by 5
  36. 2023Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settingscited by 5
  37. 2023Training-Free Neural Active Learning with Initialization-Robustness Guaranteescited by 5
  38. 2024STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Modelscited by 5

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