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When the first convolutional neural network (CNN)-based method entered the ImageNet Large-Scale Visual Recognition Challenge in 2012, its error rate was 15.3%, as compared to 25.8% for the 2011 winner. In subsequent competitions, the majority of the entries (and all of the winners) were CNN-based and continued to improve substantially, with the 2017 winner achieving an error rate of just 2.25%. Following this success, a plethora of CNN-based approaches are now being applied to a wide variety of problems in image processing, including image reconstruction problems. In this talk, I will try to provide a broader context for this trend as well as discuss my work on using learning to reconstruct images from their measurements in the context of X-ray computed tomography. Host: Marc Klasky |