mlsanityData to 14 Oct 2026

DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning

arXiv 2204.08499 ↗First posted 18 Apr 2022cs.LG cs.CVcited by 147

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Abstract (arXiv)

Coreset selection, which aims to select a subset of the most informative training samples, is a long-standing learning problem that can benefit many downstream tasks such as data-efficient learning, continual learning, neural architecture search, active learning, etc. However, many existing coreset selection methods are not designed for deep learning, which may have high complexity and poor generalization performance. In addition, the recently proposed methods are evaluated on models, datasets, and settings of different complexities. To advance the research of coreset selection in deep learning, we contribute a comprehensive code library, namely DeepCore, and provide an empirical study on popular coreset selection methods on CIFAR10 and ImageNet datasets. Extensive experiments on CIFAR10 and ImageNet datasets verify that, although various methods have advantages in certain experiment settings, random selection is still a strong baseline.

Key facts. Cited by 147 papers in the map (data through 14 Oct 2026). Builds on 37 of its 59 references that are in the map. First posted on arXiv 18 Apr 2022.

Builds on

37 of 59 references.

References · 37

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  6. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  7. 2016Wide Residual Networkscited by 3,070
  8. 2019Searching for MobileNetV3cited by 1,994
  9. 2018An Empirical Study of Example Forgetting during Deep Neural Network Learningcited by 678
  10. 2019Gradient based sample selection for online continual learningcited by 571
  11. 2021Deep Learning on a Data Diet: Finding Important Examples Early in Trainingcited by 533
  12. 2021Just Train Twice: Improving Group Robustness without Training Group Informationcited by 523
25 more
  1. 2020Dataset Condensation with Gradient Matchingcited by 516
  2. 2019Selection via Proxy: Efficient Data Selection for Deep Learningcited by 339
  3. 2021Dataset Condensation with Differentiable Siamese Augmentationcited by 291
  4. 2021GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Trainingcited by 274
  5. 2018Dataset Distillationcited by 230
  6. 2020GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learningcited by 226
  7. 2020Coresets via Bilevel Optimization for Continual Learning and Streamingcited by 192
  8. 2018Adversarial Active Learning for Deep Networks: a Margin Based Approachcited by 158
  9. 2021Active Learning by Acquiring Contrastive Examplescited by 156
  10. 2020Contextual Diversity for Active Learningcited by 139
  11. 2021Online Coreset Selection for Rehearsal-based Continual Learningcited by 117
  12. 2020Submodular Combinatorial Information Measures with Applications in Machine Learningcited by 88
  13. 2020Optimal Continual Learning has Perfect Memory and is NP-hardcited by 87
  14. 2021SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarioscited by 84
  15. 2021RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learningcited by 74
  16. 2018On Coresets for Logistic Regressioncited by 67
  17. 2019Cold Case: The Lost MNIST Digitscited by 62
  18. 2013Submodular Optimization with Submodular Cover and Submodular Knapsack Constraintscited by 41
  19. 2019Small-GAN: Speeding Up GAN Training Using Core-setscited by 41
  20. 2018Strong Coresets for k-Median and Subspace Approximation: Goodbye Dimensioncited by 27
  21. 2021Core-set Sampling for Efficient Neural Architecture Searchcited by 23
  22. 2020Semi-supervised Batch Active Learning via Bilevel Optimizationcited by 16
  23. 2021SVP-CF: Selection via Proxy for Collaborative Filtering Datacited by 14
  24. 2020On Coresets For Regularized Regressioncited by 10
  25. 2021Extending Contrastive Learning to Unsupervised Coreset Selectioncited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 33; 2024: 46; 2025: 38; 2026: 28.

147 papers.

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  1. 2023DataComp: In search of the next generation of multimodal datasetscited by 644
  2. 2023SemDeDup: Data-efficient learning at web-scale through semantic deduplicationcited by 303
  3. 2023Dataset Distillation: A Comprehensive Reviewcited by 127
  4. 2022Coverage-centric Coreset Selection for High Pruning Ratescited by 117
  5. 2023Finding Support Examples for In-Context Learningcited by 104
  6. 2023DREAM: Efficient Dataset Distillation by Representative Matchingcited by 94
  7. 2024How to Train Data-Efficient LLMscited by 93
  8. 2023Data Distillation: A Surveycited by 87
  9. 2023Anchor Points: Benchmarking Models with Much Fewer Examplescited by 86
  10. 2024Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Modelscited by 84
  11. 2023D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruningcited by 69
  12. 2023Large-scale Dataset Pruning with Dynamic Uncertaintycited by 63
38 more
  1. 2024UniTraj: A Unified Framework for Scalable Vehicle Trajectory Predictioncited by 52
  2. 2023Revisiting the Role of Language Priors in Vision-Language Modelscited by 50
  3. 2023Dataset Quantizationcited by 48
  4. 2023No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Modelscited by 41
  5. 2023Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruningcited by 38
  6. 2023Loss-Curvature Matching for Dataset Selection and Condensationcited by 33
  7. 2025PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequencecited by 33
  8. 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Modelscited by 29
  9. 2025A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advancescited by 28
  10. 2023Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learningcited by 26
  11. 2024WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Modelscited by 26
  12. 2023Uncovering Neural Scaling Laws in Molecular Representation Learningcited by 23
  13. 2023Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracycited by 20
  14. 2024Coreset Selection for Object Detectioncited by 18
  15. 2023Making Scalable Meta Learning Practicalcited by 17
  16. 2023Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sourcescited by 16
  17. 2024Diversified Batch Selection for Training Accelerationcited by 14
  18. 2024What is Dataset Distillation Learning?cited by 13
  19. 2024Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networkscited by 13
  20. 2024Data Efficient Evaluation of Large Language Models and Text-to-Image Models via Adaptive Samplingcited by 12
  21. 2025Does Training with Synthetic Data Truly Protect Privacy?cited by 12
  22. 2025LLM Unlearning Reveals a Stronger-Than-Expected Coreset Effect in Current Benchmarkscited by 12
  23. 2023Data pruning and neural scaling laws: fundamental limitations of score-based algorithmscited by 11
  24. 2023Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learningcited by 11
  25. 2024Distilling the Knowledge in Data Pruningcited by 11
  26. 2024FairDeDup: Detecting and Mitigating Vision-Language Fairness Disparities in Semantic Dataset Deduplicationcited by 11
  27. 2024SAP: Corrective Machine Unlearning with Scaled Activation Projection for Label Noise Robustnesscited by 10
  28. 2024ELFS: Label-Free Coreset Selection with Proxy Training Dynamicscited by 10
  29. 2024Speculative Coreset Selection for Task-Specific Fine-tuningcited by 10
  30. 2024Zero-Shot Coreset Selection via Iterative Subspace Samplingcited by 10
  31. 2025DD-Ranking: Rethinking the Evaluation of Dataset Distillationcited by 10
  32. 2024Beyond Efficiency: Molecular Data Pruning for Enhanced Generalizationcited by 9
  33. 2024TAROT: Targeted Data Selection via Optimal Transportcited by 9
  34. 2024Adaptive Dataset Quantizationcited by 9
  35. 2024DRoP: Distributionally Robust Data Pruningcited by 7
  36. 2024Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classificationcited by 7
  37. 2025Decouple before Align: Visual Disentanglement Enhances Prompt Tuningcited by 7
  38. 2023DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matchingcited by 6

50 most cited of 147 All 147 (JSON)