mlsanityData to 13 Oct 2026

A Survey of Dataset Refinement for Problems in Computer Vision Datasets

arXiv 2210.11717 ↗First posted 21 Oct 2022cs.CVcited by 5

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Abstract (arXiv)

Large-scale datasets have played a crucial role in the advancement of computer vision. However, they often suffer from problems such as class imbalance, noisy labels, dataset bias, or high resource costs, which can inhibit model performance and reduce trustworthiness. With the advocacy of data-centric research, various data-centric solutions have been proposed to solve the dataset problems mentioned above. They improve the quality of datasets by re-organizing them, which we call dataset refinement. In this survey, we provide a comprehensive and structured overview of recent advances in dataset refinement for problematic computer vision datasets. Firstly, we summarize and analyze the various problems encountered in large-scale computer vision datasets. Then, we classify the dataset refinement algorithms into three categories based on the refinement process: data sampling, data subset selection, and active learning. In addition, we organize these dataset refinement methods according to the addressed data problems and provide a systematic comparative description. We point out that these three types of dataset refinement have distinct advantages and disadvantages for dataset problems, which informs the choice of the data-centric method appropriate to a particular research objective. Finally, we summarize the current literature and propose potential future research topics.

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 108 of its 181 references that are in the map. First posted on arXiv 21 Oct 2022.

Builds on

108 of 181 references.

References · 108

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  2. 2020Shortcut Learning in Deep Neural Networkscited by 1,944
  3. 2017Understanding Black-box Predictions via Influence Functionscited by 1,729
  4. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
  5. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,248
  6. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,055
  7. 2017A Closer Look at Memorization in Deep Networkscited by 939
  8. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 916
  9. 2018An Empirical Study of Example Forgetting during Deep Neural Network Learningcited by 675
  10. 2018Learning to Reweight Examples for Robust Deep Learningcited by 674
  11. 2018Recognition in Terra Incognitacited by 656
  12. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 652
38 more
  1. 2019Green AIcited by 623
  2. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 621
  3. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 606
  4. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 581
  5. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 578
  6. 2019Gradient based sample selection for online continual learningcited by 570
  7. 2020A Survey of Deep Active Learningcited by 550
  8. 2021Deep Learning on a Data Diet: Finding Important Examples Early in Trainingcited by 532
  9. 2020Dataset Condensation with Gradient Matchingcited by 512
  10. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  11. 2021Dynabench: Rethinking Benchmarking in NLPcited by 452
  12. 2022Dataset Distillation by Matching Training Trajectoriescited by 438
  13. 2020Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamicscited by 424
  14. 2021Deep Long-Tailed Learning: A Surveycited by 416
  15. 2019Confident Learning: Estimating Uncertainty in Dataset Labelscited by 411
  16. 2020Balanced Meta-Softmax for Long-Tailed Visual Recognitioncited by 399
  17. 2019How does Disagreement Help Generalization against Label Corruption?cited by 390
  18. 2019Selection via Proxy: Efficient Data Selection for Deep Learningcited by 339
  19. 2020Are we done with ImageNet?cited by 331
  20. 2018Not All Samples Are Created Equal: Deep Learning with Importance Samplingcited by 327
  21. 2020An Investigation of Why Overparameterization Exacerbates Spurious Correlationscited by 310
  22. 2021GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Trainingcited by 273
  23. 2020Identifying Mislabeled Data using the Area Under the Margin Rankingcited by 260
  24. 2018Selective Experience Replay for Lifelong Learningcited by 240
  25. 2017Decoupling "when to update" from "how to update"cited by 230
  26. 2020GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learningcited by 226
  27. 2019Understanding and Utilizing Deep Neural Networks Trained with Noisy Labelscited by 209
  28. 2021Distribution Alignment: A Unified Framework for Long-tail Visual Recognitioncited by 198
  29. 2020Coresets via Bilevel Optimization for Continual Learning and Streamingcited by 191
  30. 2013Minimizing Finite Sums with the Stochastic Average Gradientcited by 164
  31. 2019SELF: Learning to Filter Noisy Labels with Self-Ensemblingcited by 164
  32. 2020Adversarial Filters of Dataset Biasescited by 160
  33. 2020Learning with Instance-Dependent Label Noise: A Sample Sieve Approachcited by 158
  34. 2019REPAIR: Removing Representation Bias by Dataset Resamplingcited by 157
  35. 2017Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samplescited by 147
  36. 2022DeepCore: A Comprehensive Library for Coreset Selection in Deep Learningcited by 147
  37. 2018CurriculumNet: Weakly Supervised Learning from Large-Scale Web Imagescited by 126
  38. 2015Online Batch Selection for Faster Training of Neural Networkscited by 122

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2024Contributing Dimension Structure of Deep Feature for Coreset Selectioncited by 5
  2. 2025Prior Distribution and Model Confidencecited by 1
  3. 2025Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selectioncited by 0
  4. 2025Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classificationcited by 0
  5. 2026RailSyn: Diagnosis-Guided Image Generation for Traceable Data Completion in Railway Foreign Object Detectioncited by 0