Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections

arXiv:2404.02954v1 Announce Type: new
Abstract: In recent years there has been increased interest in understanding the interplay between deep generative models (DGMs) and the manifold hypothesis. Research in this area focuses on understanding the reasons why commonly-used DGMs succeed or fail at learning distributions supported on unknown low-dimensional manifolds, as well as developing new models explicitly designed to account for manifold-supported data. This manifold lens provides both clarity as to why some DGMs (e.g. diffusion models and some generative adversarial …

abstract arxiv cs.ai cs.lg deep generative models dgms generative generative models hypothesis manifold research stat.ml survey through type understanding