6 citations · 14 across the 17 of their papers we have counts for
7 papers · 1 filter
Fast and Faithful: Principled Conditional Flow Matching for Inverse Problems
Shirin Shoushtari, Edward P. Chandler, Xiao Shi +1
Flow matching approaches to imaging inverse problems commonly incorporate measurements in two ways. Conditioning-based approaches supply measurement-derived information as a networ…
NullFlow: One-Step Generative Reconstruction
Xiao Shi, Edward P. Chandler, Chicago Y. Park +2
We propose NullFlow, a principled framework for one-step generative image reconstruction. Our key idea is to confine the generative flow to a measurement-consistent subspace. Becau…
EigenScore: OOD Detection using Covariance in Diffusion Models
Shirin Shoushtari, Yi Wang, Xiao Shi +2
Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems in safety-sensitive domains. Diffusion models have recently emerged as powerful…
Unsupervised Detection of Distribution Shift in Inverse Problems using Diffusion Models
Shirin Shoushtari, Edward P. Chandler, Yuanhao Wang +2
Diffusion models are widely used as priors in imaging inverse problems. However, their performance often degrades under distribution shifts between the training and test-time image…
Convergence of Nonconvex PnP-ADMM with MMSE Denoisers
Chicago Park, Shirin Shoushtari, Weijie Gan +1
Plug-and-Play Alternating Direction Method of Multipliers (PnP-ADMM) is a widely-used algorithm for solving inverse problems by integrating physical measurement models and convolut…
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
Zihao Zou, Jiaming Liu, Shirin Shoushtari +3
Face video restoration (FVR) is a challenging but important problem where one seeks to recover a perceptually realistic face videos from a low-quality input. While diffusion probab…