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.
@article{arxiv.2302.13181,
title = {Data-Copying in Generative Models: A Formal Framework},
author = {Robi Bhattacharjee and Sanjoy Dasgupta and Kamalika Chaudhuri},
journal= {arXiv preprint arXiv:2302.13181},
year = {2023}
}