6 citations · 6 across the 6 of their papers we have counts for
3 papers · 1 filter
Bilevel Learning via Strategically Inexact Stochastic Gradient Descent
Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts +1
Bilevel optimization is a central tool in computational imaging for high-dimensional hyperparameter tuning; prominent applications include learning data-adaptive regularizers and o…
Learning Regularization Functionals for Inverse Problems: A Comparative Study
Johannes Hertrich, Hok Shing Wong, Alexander Denker +16
In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insig…
Fast Inexact Bilevel Optimization for Analytical Deep Image Priors
Mohammad Sadegh Salehi, Tatiana A. Bubba, Yury Korolev
The analytical deep image prior (ADP) introduced by Dittmer et al. (2020) establishes a link between deep image priors and classical regularization theory via bilevel optimization.…