Guangyu Gao

Provably convergent proximal gradient methods with tailored input convex neural networks regularization for inverse problems

This study presents a novel Deep Proximal Gradient Descent framework for ill-posed problems by employing a tailored second-order differentiable Input-Convex Neural Networks (ICNNs) as a learned regularizer, and introduces an innovative formulation that employs the learned residual to guide gradient descent.