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

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

12 Apr 2020arXiv:2004.05675archive 2025-07-28

Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta

Detecting overfitting in generative models is an important challenge in machine learning. In this work, we formalize a form of overfitting that we call {\em{data-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

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