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SS22 WS22 SS23 WS23 SS24 WS24 Guidelines for Master Students
Variational modeling meets learning In this talk, I will show how to use learning techniques to significantly improve energy-based models. I will start by showing that even for the simplest models such as total variation, one can greatly improve the accuracy of the numerical approximation by learning the „best“ discretization within a class of consistent discretizations. Then I will move forward to more expressive models and show how they can be learned in order to give state-of-the art performance for image reconstruction problems, such as denoising, superresolution, MRI and CT. |