36 citations · 79 across the 16 of their papers we have counts for
5 papers · 1 filter
A Geometric Framework for Understanding Memorization in Generative Models
Brendan Leigh Ross, Hamidreza Kamkari, Tongzi Wu +5
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 questi…
Verifying the Union of Manifolds Hypothesis for Image Data
Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross +2
Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data. This success would be impossible if there was no hidden low-dimension…
Neural Implicit Manifold Learning for Topology-Aware Density Estimation
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini +1
Natural data observed in is often constrained to an -dimensional manifold , where . This work focuses on the task of building theoretically pr…
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell +1
Likelihood-based, or explicit, deep generative models use neural networks to construct flexible high-dimensional densities. This formulation directly contradicts the manifold hypot…
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows
Brendan Leigh Ross, Jesse C. Cresswell
Normalizing flows are generative models that provide tractable density estimation via an invertible transformation from a simple base distribution to a complex target distribution.…