Oral: Task adapted reconstruction for inverse problems

Abstract

We consider the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem. Several such tasks have been approached using deep neural networks, and recent advancements in image reconstruction using learned iterative schemes now enable us to have a fully differentiable, end-to-end trainable, imaging pipeline. The suggested framework is adaptable, with a plug-and-play structure for adjusting to both the inverse problem and the task at hand. The approach is demonstrated on joint tomographic image reconstruction and semantic segmentation.