1 Paper accepted to CVPR 2022

Abstract

Paper titled “Come-Closer-Diffuse-Faster Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction” is accepted to CVPR 2022. We study the stochastically contracting property of reverse diffusion, and leverage this property to significantly accelerate diffusion model based linear inverse problem solvers.

Hyungjin Chung, Ph.D.

Research Scientist

Generative models, Inverse problems, Multimodality, Motion, and more.