pith. machine review for the scientific record. sign in

arxiv: 1811.03659 · v1 · submitted 2018-11-08 · 📡 eess.SP · cs.LG

Recognition: unknown

Plug-In Stochastic Gradient Method

Yu Sun , Brendt Wohlberg , Ulugbek S. Kamilov
Authors on Pith no claims yet
classification 📡 eess.SP cs.LG
keywords onlineadditionallyadvancedalgorithmalgorithmsbatchconvergencedatasets
0
0 comments X
read the original abstract

Plug-and-play priors (PnP) is a popular framework for regularized signal reconstruction by using advanced denoisers within an iterative algorithm. In this paper, we discuss our recent online variant of PnP that uses only a subset of measurements at every iteration, which makes it scalable to very large datasets. We additionally present novel convergence results for both batch and online PnP algorithms.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Stochastic Generative Plug-and-Play Priors

    cs.CV 2026-04 conditional novelty 6.0

    Noise injection into plug-and-play algorithms using pretrained score-based diffusion denoisers optimizes a Gaussian-smoothed objective and yields better reconstructions for severely ill-posed imaging tasks.