[andthenwhat]

How do you avoid the problem of using data-trained algorithms to provide measurements (in that they can’t)?

The idea of classifying or localizing detections with deep learning (or any data-trained approach) seems totally reasonable, since it’s clearly making an inference, and that should be clear to the human user. Enhancing or gap-filling the measurements with data-trained approaches would turn the measurements themselves into inference, which seems in opposition of the diagnostic goals (algorithm in-painting non-sensed information learned from the training set)

[andbberger]

carefully. that's the whole problem. the beauty of compressed sensing is that it comes with a convergence guarantee. figuring out how to provide similar (but probably not as strong guarantees) with more sophisticated models of the prior is what is going to allow this to work.