One really contrived usecase may be "flame graphs" for high level diagnostics of learned models in a production pipeline, as you collect more data or modify fea

One really contrived usecase may be "flame graphs" for high level diagnostics of learned models in a production pipeline, as you collect more data or modify features. But I don't imagine it'd be very good. - the images and false colors need to show some semblance of stability for a given network between epochs; and it needs to be robust against changing data or input structure. - requiring visual inspection doesn't give you something you can automate with, unlike an evaluation score. - if there is indeed a significant deviation in "MRI"-like scans between batches, its diagnostic utility ends there - it tells you nothing about what caused a change.