2 Papers accepted to NeurIPS 2022 / NeurIPS 2022 SBM Workshop

Abstract

Paper titled “Improving Diffusion Models for Inverse Problems using Manifold Constraints” is accepted to NeurIPS 2022. We propose a geometric view of diffusion models, and use this view to dramatically improve the performance of diffusion models in linear inverse problem solving. Paper titled “Progressive deblurring of diffusion models for coarse-to-fine image synthesis” is accepted to NeurIPS 2022 SBM workshop.

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.