6 citations · 14 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
Moments Matter: Posterior Recovery in Poisson Denoising via Log-Networks
Shirin Shoushtari, Edward P. Chandler, Ulugbek S. Kamilov
Poisson denoising plays a central role in photon-limited imaging applications such as microscopy, astronomy, and medical imaging. It is common to train deep learning models for den…
Analysis Plug-and-Play Methods for Imaging Inverse Problems
Edward P. Chandler, Shirin Shoushtari, Brendt Wohlberg +1
Plug-and-Play Priors (PnP) is a popular framework for solving imaging inverse problems by integrating learned priors in the form of denoisers trained to remove Gaussian noise from…
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…
Diff-Unfolding: A Model-Based Score Learning Framework for Inverse Problems
Yuanhao Wang, Shirin Shoushtari, Ulugbek S. Kamilov
Diffusion models are extensively used for modeling image priors for inverse problems. We introduce \emph{Diff-Unfolding}, a principled framework for learning posterior score functi…
Measurement Score-Based Diffusion Model
Chicago Y. Park, Shirin Shoushtari, Hongyu An +1
Diffusion models are widely used in applications ranging from image generation to inverse problems. However, training diffusion models typically requires clean ground-truth images,…