A Geometric Framework for Understanding Memorization in Generative Models
Brendan Leigh Ross Hamidreza Kamkari Tongzi Wu Rasa Hosseinzadeh Zhaoyan Liu George Stein Jesse C. Cresswell Gabriel Loaiza-Ganem
As deep generative models have progressed, recent work has shown them to be capable of memorizing and reproducing training datapoints when deployed. These findings call into question the usability of generative models, especially in light of the legal and privacy risks brought about by memorization. To better understand this phenomenon, we propose the *manifold memorization hypothesis* (MMH), a geometric framework which leverages the manifold hypothesis into a clear language in which to reason about memorization. We propose to analyze memorization in terms of the relationship between the dimensionalities of $(i)$ the ground truth data manifold and $(ii)$ the manifold learned by the model. This framework provides a formal standard for "how memorized" a datapoint is and systematically categorizes memorized data into two types: memorization driven by overfitting and memorization driven by the underlying data distribution. By analyzing prior work in the context of the MMH, we explain and unify assorted observations in the literature. We empirically validate the MMH using synthetic data and image datasets up to the scale of Stable Diffusion, developing new tools for detecting and preventing generation of memorized samples in the process.
deep generative modelling, generative models, memorization, data copying, privacy, diffusion, diffusion models, GANs, manifold hypothesis, local intrinsic dimension, lid, lid estimation, geometry
We show that the manifold hypothesis is a useful way to understand memorization in generative models