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Diverse mini-batch Active Learning

arXiv 1901.05954 ↗First posted 17 Jan 2019cs.LG stat.MLcited by 89

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Abstract (arXiv)

We study the problem of reducing the amount of labeled training data required to train supervised classification models. We approach it by leveraging Active Learning, through sequential selection of examples which benefit the model most. Selecting examples one by one is not practical for the amount of training examples required by the modern Deep Learning models. We consider the mini-batch Active Learning setting, where several examples are selected at once. We present an approach which takes into account both informativeness of the examples for the model, as well as the diversity of the examples in a mini-batch. By using the well studied K-means clustering algorithm, this approach scales better than the previously proposed approaches, and achieves comparable or better performance.

Key facts. Cited by 89 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jan 2019.

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Citing papers per year: 2021: 16; 2022: 25; 2023: 24; 2024: 17; 2025: 3; 2026: 4.

89 papers.

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  1. 2021Active Learning by Acquiring Contrastive Examplescited by 156
  2. 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 133
  3. 2021Batch Active Learning at Scalecited by 127
  4. 2022A Comparative Survey of Deep Active Learningcited by 89
  5. 2022Active Learning Through a Covering Lenscited by 66
  6. 2022Data Curation Alone Can Stabilize In-context Learningcited by 59
  7. 2024Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approachcited by 50
  8. 2022A Framework and Benchmark for Deep Batch Active Learning for Regressioncited by 49
  9. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  10. 2021Deep Active Learning by Leveraging Training Dynamicscited by 32
  11. 2022Plug and Play Active Learning for Object Detectioncited by 28
  12. 2021Data Shapley Valuation for Efficient Batch Active Learningcited by 27
38 more
  1. 2021Cartography Active Learningcited by 26
  2. 2023The Efficiency Spectrum of Large Language Models: An Algorithmic Surveycited by 26
  3. 2022A Lagrangian Duality Approach to Active Learningcited by 24
  4. 2021Neural Active Learning with Performance Guaranteescited by 22
  5. 2021Diversity Enhanced Active Learning with Strictly Proper Scoring Rulescited by 22
  6. 2022MEAL: Stable and Active Learning for Few-Shot Promptingcited by 20
  7. 2021Discrepancy-Based Active Learning for Domain Adaptationcited by 17
  8. 2022Active Learning Strategies for Weakly-supervised Object Detectioncited by 17
  9. 2021On Data Efficiency of Meta-learningcited by 16
  10. 2022Fast Rates in Pool-Based Batch Active Learningcited by 15
  11. 2024Uncertainty Herding: One Active Learning Method for All Label Budgetscited by 15
  12. 2023D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Biascited by 12
  13. 2021Multi-class Text Classification using BERT-based Active Learningcited by 11
  14. 2022Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regressioncited by 11
  15. 2022SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detectioncited by 10
  16. 2023Best Practices in Active Learning for Semantic Segmentationcited by 10
  17. 2024Neural Active Learning Beyond Banditscited by 10
  18. 2021Deep Indexed Active Learning for Matching Heterogeneous Entity Representationscited by 9
  19. 2023On the Limitations of Simulating Active Learningcited by 9
  20. 2023An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mappingcited by 8
  21. 2022Deep Active Learning for Computer Vision: Past and Futurecited by 7
  22. 2024AHA: Human-Assisted Out-of-Distribution Generalization and Detectioncited by 7
  23. 2021Multi-Domain Active Learning: Literature Review and Comparative Studycited by 6
  24. 2022Teaching Robots to Span the Space of Functional Expressive Motioncited by 6
  25. 2022An Empirical Study on the Efficacy of Deep Active Learning for Image Classificationcited by 6
  26. 2023Investigating Multi-source Active Learning for Natural Language Inferencecited by 6
  27. 2023Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learningcited by 6
  28. 2024SCAR: Data Selection via Style Consistency-Aware Response Ranking for Efficient Instruction-Tuning of Large Language Modelscited by 6
  29. 2024Out-of-Distribution Learning with Human Feedbackcited by 6
  30. 2022TrustAL: Trustworthy Active Learning using Knowledge Distillationcited by 5
  31. 2024Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samplescited by 5
  32. 2021Sample Noise Impact on Active Learningcited by 4
  33. 2022Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discoverycited by 4
  34. 2022Improved Adaptive Algorithm for Scalable Active Learning with Weak Labelercited by 4
  35. 2023Query Complexity of Active Learning for Function Family With Nearly Orthogonal Basiscited by 4
  36. 2023Active Learning for Object Detection with Non-Redundant Informative Samplingcited by 4
  37. 2023Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasetscited by 4
  38. 2023Active Transfer Learning for Efficient Video-Specific Human Pose Estimationcited by 4

50 most cited of 89 All 89 (JSON)