Oral: Task adapted reconstruction for inverse problems


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.

Montreal, Canada
Jonas Adler
Research Scientist