Solving various domain translation problems using deep convolutional framelets
Jaejun Yoo

Meeting • 11 February 2020

Abstract
Domain translation is a general category that subsumes various problems, such as image-to-image translation, style transfer, super-resolution and even inverse problems in some sense. In this talk, I first introduce deep convolutional framelets, which is the main tool we used to solve domain translation problems. My recent works on photorealistic style transfer (ICCV '19) and inverse scattering problems (SIAM '18, TMI '19) will be presented. I provide a sketch of ideas behind the theory, which bridges the relationship between the signal processing and the U-Net type architectures that are prevalent in recent deep learning studies. Based on these understandings, we provide a simple but effective correction to a network architecture that is not only theoretically sound but remarkably enhancing the performance in practice.