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Found 6 papers, 2 papers with code
A Neural-Network-Based Convex Regularizer for Inverse Problems
The emergence of deep-learning-based methods to solve image-reconstruction problems has enabled a significant increase in reconstruction quality.
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Lipschitz-constrained neural networks have several advantages over unconstrained ones and can be applied to a variety of problems, making them a topic of attention in the deep learning community.
Approximation of Lipschitz Functions using Deep Spline Neural Networks
Lipschitz-constrained neural networks have many applications in machine learning.
Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors
In this work, we present a Bayesian reconstruction framework for nonlinear imaging models where we specify the prior knowledge on the image through a deep generative model.
A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods
We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data.
Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods
We show that averaged denoising operators built from 1-Lipschitz deep spline networks consistently outperform those built from 1-Lipschitz ReLU networks.
