mlsanityData to 7 Oct 2026

Data-Copying in Generative Models: A Formal Framework

arXiv 2302.13181 ↗First posted 25 Feb 2023cs.LGcited by 9

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Abstract (arXiv)

There has been some recent interest in detecting and addressing memorization of training data by deep neural networks. A formal framework for memorization in generative models, called "data-copying," was proposed by Meehan et. al. (2020). We build upon their work to show that their framework may fail to detect certain kinds of blatant memorization. Motivated by this and the theory of non-parametric methods, we provide an alternative definition of data-copying that applies more locally. We provide a method to detect data-copying, and provably show that it works with high probability when enough data is available. We also provide lower bounds that characterize the sample requirement for reliable detection.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 25 Feb 2023.

Builds on

13 of 21 references.

References · 13

  1. 2016Improved Techniques for Training GANscited by 2,938
  2. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  3. 2022Quantifying Memorization Across Neural Language Modelscited by 755
  4. 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 452
  5. 2018Assessing Generative Models via Precision and Recallcited by 401
  6. 2016Revisiting Classifier Two-Sample Testscited by 258
  7. 2020When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?cited by 102
  8. 2018An empirical study on evaluation metrics of generative adversarial networkscited by 81
  9. 2018On GANs and GMMscited by 62
  10. 2021On Memorization in Probabilistic Deep Generative Modelscited by 60
  11. 2021Intrinsic Dimension Estimation Using Wasserstein Distancescited by 20
  12. 2021On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competitioncited by 18
1 more
  1. 2020Empirical Analysis of Overfitting and Mode Drop in GAN Trainingcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 3; 2026: 1.

9 papers.

  1. 2025How much do language models memorize?cited by 53
  2. 2024A Geometric Framework for Understanding Memorization in Generative Modelscited by 40
  3. 2024Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapescited by 20
  4. 2024Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Modelscited by 12
  5. 2024Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Datacited by 5
  6. 2025On Convolutions, Intrinsic Dimension, and Diffusion Modelscited by 2
  7. 2024Local Lesion Generation is Effective for Capsule Endoscopy Image Data Augmentation in a Limited Data Settingcited by 1
  8. 2025Generation Properties of Stochastic Interpolation under Finite Training Setcited by 1
  9. 2026The Null Is the Hard Part: Exact Tests for Memorization in Generative Modelscited by 0