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Found 3 papers, 2 papers with code
On Generalization Bounds for Deep Compound Gaussian Neural Networks
In this paper, we develop novel generalization error bounds for a class of unrolled DNNs that are informed by a compound Gaussian prior.
Deep Regularized Compound Gaussian Network for Solving Linear Inverse Problems
Incorporating prior information into inverse problems, e. g. via maximum-a-posteriori estimation, is an important technique for facilitating robust inverse problem solutions.
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems
The first approach is an iterative algorithm that minimizes a regularized least squares objective function where the regularization is based on a compound Gaussian prior distribution.
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