221 citations · 1.5k across the 104 of their papers we have counts for
26 papers · 1 filter
SNaP: One-Step Posterior Sampling for Noisy Inverse Problems
Shirin Shoushtari, Edward P. Chandler, Xiao Shi +1
Diffusion and flow-matching models can produce high-quality posterior samples for inverse problems, but typically require tens to thousands of network evaluations per draw. MeanFlo…
FIRM: Flow-based Imaging via Regularized Minimization
Shirin Shoushtari, Edward P. Chandler, Xiao Shi +1
Flow matching methods for imaging inverse problems typically incorporate measurements through network conditioning or guidance during sampling. Neither approach explicitly applies…
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…
Stochastic Generative Plug-and-Play Priors
Chicago Y. Park, Edward P. Chandler, Yuyang Hu +4
Plug-and-play (PnP) methods are widely used for solving imaging inverse problems by incorporating a denoiser into optimization algorithms. Score-based diffusion models (SBDMs) have…
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…
Kernel Density Steering: Inference-Time Scaling via Mode Seeking for Image Restoration
Yuyang Hu, Kangfu Mei, Mojtaba Sahraee-Ardakan +3
Diffusion models show promise for image restoration, but existing methods often struggle with inconsistent fidelity and undesirable artifacts. To address this, we introduce Kernel…