It looks very adhoc
So ideally the ml network would solve the problem end to end but these authors seem to be using a network for only one step of their otherwise classic image processing pipeline
So ideally the ml network would solve the problem end to end but these authors seem to be using a network for only one step of their otherwise classic image processing pipeline
A lot of applied work in vision and audio glues together different existing modules, instead of training the whole thing end-to-end.
In an ideal world, things are ideal. But the world isn't a grad student's wet dream.
But there are not as many influential papers in the world as there are people who respond: "Why didn't they train the whole thing end-to-end?"