Wasserstein proximal operators describe score-based generative models
and resolve memorization

Wasserstein proximal operators describe score-based generative models and resolve memorization

9 February 2024

Benjamin J. Zhang

Wuchen Li

Stanley J. Osher

Papers citing "Wasserstein proximal operators describe score-based generative models and resolve memorization"

7 / 7 papers shown

Title
Shallow Diffuse: Robust and Invisible Watermarking through Low-Dimensional Subspaces in Diffusion Models Wenda Li Huijie Zhang Qing Qu WIGM 38 0 0 28 Oct 2024
Equivariant score-based generative models provably learn distributions with symmetries efficiently Ziyu Chen M. Katsoulakis Benjamin J. Zhang DiffM 17 2 0 02 Oct 2024
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models Tingwei Meng Zongren Zou Jérome Darbon George Karniadakis DiffM 30 2 0 15 Sep 2024
Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows Hyemin Gu M. Katsoulakis Luc Rey-Bellet Benjamin J. Zhang 26 2 0 16 Jul 2024
Score-based generative models are provably robust: an uncertainty quantification perspective Nikiforos Mimikos-Stamatopoulos Benjamin J. Zhang M. Katsoulakis DiffM 18 6 0 24 May 2024
A Good Score Does not Lead to A Good Generative Model Sixu Li Shi Chen Qin Li DiffM 53 15 0 10 Jan 2024
Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data Hyemin Gu P. Birmpa Yannis Pantazis Luc Rey-Bellet M. Katsoulakis 11 2 0 31 Oct 2022