223 citations · 955 across the 36 of their papers we have counts for
4 papers · 1 filter
GENIE: Higher-Order Denoising Diffusion Solvers
Tim Dockhorn, Arash Vahdat, Karsten Kreis
Denoising diffusion models (DDMs) have emerged as a powerful class of generative models. A forward diffusion process slowly perturbs the data, while a deep model learns to graduall…
Differentially Private Diffusion Models
Tim Dockhorn, Tianshi Cao, Arash Vahdat +1
While modern machine learning models rely on increasingly large training datasets, data is often limited in privacy-sensitive domains. Generative models trained with differential p…
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
Tim Dockhorn, Arash Vahdat, Karsten Kreis
Score-based generative models (SGMs) have demonstrated remarkable synthesis quality. SGMs rely on a diffusion process that gradually perturbs the data towards a tractable distribut…
Score-based Generative Modeling in Latent Space
Arash Vahdat, Karsten Kreis, Jan Kautz
Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage. However, they are usually applied dire…