Rebecca Willett: "Learning to Solve Inverse Problems in Imaging" скачать в хорошем качестве

Rebecca Willett: "Learning to Solve Inverse Problems in Imaging"

High Dimensional Hamilton-Jacobi PDEs 2020 Workshop II: PDE and Inverse Problem Methods in Machine Learning "Learning to Solve Inverse Problems in Imaging" Rebecca Willett - University of Chicago Abstract: Traditional inverse problem solvers in imaging minimize a cost function consisting of a data-fit term, which measures how well an image matches the observations, and a regularizer, which reflects prior knowledge and promotes images with desirable properties like smoothness. Recent advances in machine learning and image processing have illustrated that it is often possible to learn a regularizer from training data that can outperform more traditional regularizers. In this talk, I will describe various classes of approaches to learned regularization, ranging from generative models to unrolled optimization perspectives, and explore their relative merits and tradeoffs. Institute for Pure and Applied Mathematics, UCLA April 23, 2020 For more information: https://www.ipam.ucla.edu/hjws2