mlsanityData to 7 Oct 2026

A Non-Parametric Test to Detect Data-Copying in Generative Models

arXiv 2004.05675 ↗First posted 12 Apr 2020cs.LG stat.MLcited by 52

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Abstract (arXiv)

Detecting overfitting in generative models is an important challenge in machine learning. In this work, we formalize a form of overfitting that we call emdata-copying -- where the generative model memorizes and outputs training samples or small variations thereof. We provide a three sample non-parametric test for detecting data-copying that uses the training set, a separate sample from the target distribution, and a generated sample from the model, and study the performance of our test on several canonical models and datasets. For code & examples, visit https://github.com/casey-meehan/data-copying

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Apr 2020.

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Citing papers per year: 2021: 7; 2022: 4; 2023: 11; 2024: 13; 2025: 12; 2026: 5.

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  1. 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 481
  2. 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 418
  3. 2023Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion modelscited by 199
  4. 2021How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Modelscited by 193
  5. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 121
  6. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  7. 2023REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformerscited by 93
  8. 2022Do Language Models Plagiarize?cited by 72
  9. 2021On Memorization in Probabilistic Deep Generative Modelscited by 60
  10. 2024Erasing Undesirable Influence in Diffusion Modelscited by 52
  11. 2024A Geometric Framework for Understanding Memorization in Generative Modelscited by 40
  12. 2023A Pathway Towards Responsible AI Generated Contentcited by 38
38 more
  1. 2023DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathologycited by 28
  2. 2023Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samplescited by 25
  3. 2023Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learningcited by 24
  4. 2021Understanding Instance-based Interpretability of Variational Auto-Encoderscited by 21
  5. 2021On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competitioncited by 18
  6. 2024An Interpretable Evaluation of Entropy-based Novelty of Generative Modelscited by 17
  7. 2024Unveiling Differences in Generative Models: A Scalable Differential Clustering Approachcited by 16
  8. 2025Metrics that matter: Evaluating image quality metrics for medical image generationcited by 12
  9. 2025Generative Modeling of Weights: Generalization or Memorization?cited by 12
  10. 2024Frame by Familiar Frame: Understanding Replication in Video Diffusion Modelscited by 11
  11. 2024Forte : Finding Outliers with Representation Typicality Estimationcited by 9
  12. 2022Generalization Metrics for Practical Quantum Advantage in Generative Modelscited by 8
  13. 2021DP-SGD vs PATE: Which Has Less Disparate Impact on GANs?cited by 6
  14. 2025The Vendiscope: An Algorithmic Microscope For Data Collectionscited by 5
  15. 2022Reducing Training Sample Memorization in GANs by Training with Memorization Rejectioncited by 4
  16. 2024Data Pruning in Generative Diffusion Modelscited by 3
  17. 2025GReaTER: Generate Realistic Tabular data after data Enhancement and Reductioncited by 3
  18. 2023Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generationcited by 2
  19. 2023Preserving Privacy in GANs Against Membership Inference Attackcited by 2
  20. 2024DEREC-SIMPRO: unlock Language Model benefits to advance Synthesis in Data Clean Roomcited by 2
  21. 2025Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysiscited by 2
  22. 2024Detecting Generative Parroting through Overfitting Masked Autoencoderscited by 1
  23. 2024PDFed: Privacy-Preserving and Decentralized Asynchronous Federated Learning for Diffusion Modelscited by 1
  24. 2024Local Lesion Generation is Effective for Capsule Endoscopy Image Data Augmentation in a Limited Data Settingcited by 1
  25. 2025Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approachcited by 1
  26. 2025Generation Properties of Stochastic Interpolation under Finite Training Setcited by 1
  27. 2025PRIVET: PRoximIty leakage detection Via Extreme value Theorycited by 1
  28. 2025Memorization in 3D Shape Generation: An Empirical Studycited by 1
  29. 2023Improving few-shot learning-based protein engineering with evolutionary samplingcited by 0
  30. 2023Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objectivecited by 0
  31. 2024Embedding Space Selection for Detecting Memorization and Fingerprinting in Generative Modelscited by 0
  32. 2024GeFL: Model-Agnostic Federated Learning with Generative Modelscited by 0
  33. 2025PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Modelscited by 0
  34. 2025Boosting Statistic Learning with Synthetic Data from Pretrained Large Modelscited by 0
  35. 2025STEB: In Search of the Best Evaluation Approach for Synthetic Time Seriescited by 0
  36. 2026A Calibrated Memorization Index (MI) for Detecting Training Data Leakage in Generative MRI Modelscited by 0
  37. 2026Do Generative Metrics Predict YOLO Performance? An Evaluation Across Models, Augmentation Ratios, and Dataset Complexitycited by 0
  38. 2026Coreset-Induced Conditional Velocity Flow Matchingcited by 0

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