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Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

arXiv 1906.03671 ↗First posted 9 Jun 2019cs.LG stat.MLcited by 602

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Abstract (arXiv)

We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both predictive uncertainty and sample diversity into every selected batch. Crucially, BADGE trades off between diversity and uncertainty without requiring any hand-tuned hyperparameters. We show that while other approaches sometimes succeed for particular batch sizes or architectures, BADGE consistently performs as well or better, making it a versatile option for practical active learning problems.

Key facts. Cited by 602 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2019.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 68; 2022: 101; 2023: 134; 2024: 112; 2025: 93; 2026: 93.

602 papers.

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  1. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  2. 2021GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Trainingcited by 269
  3. 2024ShieldGemma: Generative AI Content Moderation Based on Gemmacited by 224
  4. 2021Active Learning by Acquiring Contrastive Examplescited by 154
  5. 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 131
  6. 2021Batch Active Learning at Scalecited by 127
  7. 2022Coverage-centric Coreset Selection for High Pruning Ratescited by 115
  8. 2021Gone Fishing: Neural Active Learning with Fisher Embeddingscited by 88
  9. 2022A Comparative Survey of Deep Active Learningcited by 88
  10. 2021SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarioscited by 83
  11. 2022Efficient Methods for Natural Language Processing: A Surveycited by 78
  12. 2023LLMaAA: Making Large Language Models as Active Annotatorscited by 78
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  1. 2024Data Shapley in One Training Runcited by 76
  2. 2021On Statistical Bias In Active Learning: How and When To Fix Itcited by 72
  3. 2021RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learningcited by 72
  4. 2023D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruningcited by 68
  5. 2023Data Pruning via Moving-one-Sample-outcited by 67
  6. 2022Active Learning by Feature Mixingcited by 63
  7. 2023FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Informationcited by 63
  8. 2022Active Learning Through a Covering Lenscited by 62
  9. 2023Prediction-Oriented Bayesian Active Learningcited by 60
  10. 2021Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentationcited by 56
  11. 2021Active Learning for Domain Adaptation: An Energy-Based Approachcited by 53
  12. 2022Entropy-based Active Learning for Object Detection with Progressive Diversity Constraintcited by 53
  13. 2021ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentationcited by 52
  14. 2021PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selectioncited by 50
  15. 2024Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approachcited by 48
  16. 2022A Framework and Benchmark for Deep Batch Active Learning for Regressioncited by 47
  17. 2024Active Statistical Inferencecited by 46
  18. 2021Reducing Label Effort: Self-Supervised meets Active Learningcited by 42
  19. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  20. 2025Prompt Curriculum Learning for Efficient LLM Post-Trainingcited by 40
  21. 2025Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoningcited by 38
  22. 2021Effective Evaluation of Deep Active Learning on Image Classification Taskscited by 37
  23. 2022Submodularity In Machine Learning and Artificial Intelligencecited by 37
  24. 2021Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learningcited by 36
  25. 2021Deep Active Learning by Leveraging Training Dynamicscited by 32
  26. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  27. 2021On the Importance of Effectively Adapting Pretrained Language Models for Active Learningcited by 31
  28. 2024Active Generalized Category Discoverycited by 31
  29. 2024PINNACLE: PINN Adaptive ColLocation and Experimental points selectioncited by 31
  30. 2022GALAXY: Graph-based Active Learning at the Extremecited by 29
  31. 2023Active learning for data streams: a surveycited by 29
  32. 2021Active Learning at the ImageNet Scalecited by 28
  33. 2022Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantitiescited by 28
  34. 2022Plug and Play Active Learning for Object Detectioncited by 28
  35. 2024Diversity Measurement and Subset Selection for Instruction Tuning Datasetscited by 28
  36. 2021Data Shapley Valuation for Efficient Batch Active Learningcited by 27
  37. 2021TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Informationcited by 27
  38. 2023Exploring Active 3D Object Detection from a Generalization Perspectivecited by 26

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