mlsanityData to 7 Oct 2026

Batch Active Learning at Scale

arXiv 2107.14263 ↗First posted 29 Jul 2021cs.LG cs.AIcited by 127

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Abstract (arXiv)

The ability to train complex and highly effective models often requires an abundance of training data, which can easily become a bottleneck in cost, time, and computational resources. Batch active learning, which adaptively issues batched queries to a labeling oracle, is a common approach for addressing this problem. The practical benefits of batch sampling come with the downside of less adaptivity and the risk of sampling redundant examples within a batch -- a risk that grows with the batch size. In this work, we analyze an efficient active learning algorithm, which focuses on the large batch setting. In particular, we show that our sampling method, which combines notions of uncertainty and diversity, easily scales to batch sizes (100K-1M) several orders of magnitude larger than used in previous studies and provides significant improvements in model training efficiency compared to recent baselines. Finally, we provide an initial theoretical analysis, proving label complexity guarantees for a related sampling method, which we show is approximately equivalent to our sampling method in specific settings.

Key facts. Cited by 127 papers in the map (data through 7 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 29 Jul 2021.

Builds on

16 of 31 references.

References · 16

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
  3. 2019On Exact Computation with an Infinitely Wide Neural Netcited by 572
  4. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  5. 2019Learning Loss for Active Learningcited by 335
  6. 2019Variational Adversarial Active Learningcited by 284
  7. 2020GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learningcited by 224
  8. 2014Lazier Than Lazy Greedycited by 97
  9. 2019Deep Active Learning: Unified and Principled Method for Query and Trainingcited by 92
  10. 2019Diverse mini-batch Active Learningcited by 88
  11. 2020Task-Aware Variational Adversarial Active Learningcited by 84
  12. 2021Evaluating Large-Vocabulary Object Detectors: The Devil is in the Detailscited by 57
4 more
  1. 2021Data Shapley Valuation for Efficient Batch Active Learningcited by 27
  2. 2021Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noisecited by 18
  3. 2021Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasetscited by 11
  4. 2018Ranking CGANs: Subjective Control over Semantic Image Attributescited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 22; 2023: 32; 2024: 34; 2025: 17; 2026: 20.

127 papers.

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  1. 2024ShieldGemma: Generative AI Content Moderation Based on Gemmacited by 224
  2. 2022Coverage-centric Coreset Selection for High Pruning Ratescited by 115
  3. 2022A Comparative Survey of Deep Active Learningcited by 88
  4. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 53
  5. 2023Self-Evolved Diverse Data Sampling for Efficient Instruction Tuningcited by 52
  6. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  7. 2024Reinforcement Learning from Human Feedback with Active Queriescited by 39
  8. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  9. 2022GALAXY: Graph-based Active Learning at the Extremecited by 29
  10. 2023Active learning for data streams: a surveycited by 29
  11. 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Modelscited by 29
  12. 2021Active Learning at the ImageNet Scalecited by 28
38 more
  1. 2022Plug and Play Active Learning for Object Detectioncited by 28
  2. 2025A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advancescited by 28
  3. 2023Exploring Active 3D Object Detection from a Generalization Perspectivecited by 26
  4. 2023Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessmentcited by 25
  5. 2022A Lagrangian Duality Approach to Active Learningcited by 24
  6. 2022Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learningcited by 24
  7. 2024An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Modelscited by 22
  8. 2022Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergycited by 20
  9. 2023ScatterShot: Interactive In-context Example Curation for Text Transformationcited by 20
  10. 2023LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learningcited by 20
  11. 2023Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracycited by 20
  12. 2023Algorithm Selection for Deep Active Learning with Imbalanced Datasetscited by 18
  13. 2025From Selection to Generation: A Survey of LLM-based Active Learningcited by 18
  14. 2022Fast Rates in Pool-Based Batch Active Learningcited by 15
  15. 2024Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyondcited by 15
  16. 2022Deep Active Learning with Noise Stabilitycited by 13
  17. 2022Active Learning with Neural Networks: Insights from Nonparametric Statisticscited by 13
  18. 2023KECOR: Kernel Coding Rate Maximization for Active 3D Object Detectioncited by 13
  19. 2023D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Biascited by 12
  20. 2022Improved Algorithms for Neural Active Learningcited by 11
  21. 2022Is margin all you need? An extensive empirical study of active learning on tabular datacited by 10
  22. 2024Neural Active Learning Beyond Banditscited by 10
  23. 2022Robust Active Distillationcited by 9
  24. 2023Deep Anomaly Detection under Labeling Budget Constraintscited by 9
  25. 2024Zero-Shot Coreset Selection via Iterative Subspace Samplingcited by 9
  26. 2023Agile Modeling: From Concept to Classifier in Minutescited by 8
  27. 2023Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separationcited by 8
  28. 2022Deep Active Learning for Computer Vision: Past and Futurecited by 7
  29. 2024Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitionscited by 7
  30. 2024Querying Easily Flip-flopped Samples for Deep Active Learningcited by 7
  31. 2024AHA: Human-Assisted Out-of-Distribution Generalization and Detectioncited by 7
  32. 2025Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detectioncited by 7
  33. 2022Promises and Pitfalls of Threshold-based Auto-labelingcited by 6
  34. 2022An Empirical Study on the Efficacy of Deep Active Learning for Image Classificationcited by 6
  35. 2023Active Learning for Abstractive Text Summarizationcited by 6
  36. 2023Pareto Optimization to Accelerate Multi-Objective Virtual Screeningcited by 6
  37. 2024Out-of-Distribution Learning with Human Feedbackcited by 6
  38. 2024Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Costcited by 5

50 most cited of 127 All 127 (JSON)