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You can always "buy" performance by either training on more data, better data, or by injecting task information into the architecture or the preprocessing. However, this isn't informative about the generalization power of the techniques used (which is the only thing that matters)
Basically, a lot of papers can be rephrased as "we achieved better performance on this specific task by going to great lengths to inject more information about the task in our training setup"
An extreme case of this: working with a synthetically-generated dataset where samples follow a "template" (e.g. bAbI), and manually hard-coding that template into your NN architecture
Fitting parametric models via gradient descent, unsurprisingly, works best when what you are fitting is already a template of the solution. Of course, convnets are an instance of this (but in a good way, since their assumptions generalize to all visual data).
This, as a meta-result, is very interesting though, isn't it? Having meaningful priors gives you an advantage. Now one would need to find a general language of priors that generalizes well.
I've done this! Not proud of it, but, I've done this. Had to get a paper published as part of a course. Ran out of actual ideas (which didn't actual achieve anything)
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