Paper accepted to NeurIPS 2023

Abstract

Paper titled “Direct Diffusion Bridges using Data Consistency for Inverse Problems” is accepted to NeurIPS 2023. We unify the seemingly different frameworks into one, under the name direct diffusion bridges. We further move on to propose data consistent direct diffusion bridge, which can push the pareto-frontier forward by imposing data consistency.

Hyungjin Chung

Ph.D. student - Generative Models & Inverse Problems

My research interests include, but is not restricted to developing efficient, modular deep generative models (diffusion models), and solving real-world inverse problems (MRI, tomography, microscopy, phase retrieval, etc.) with deep generative priors.