  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
