[dimatura]

What is the "pompous deep learning bullshit"? They don't really dwell a lot on DL itself, just describe a reasonable architecture for the task and how they train it. While it's possible to conceive the same concept realized with other machine learning methods (e.g. Random Forest or linear embeddings), it's hard to imagine it would work as well.

As for overselling, I'd say that it's somewhat standard writing style in CS academia to oversell[1] but this is hardly an example of that.

[1] It'd be unusual, but admittedly refreshing for a paper to say "this is just an incremental tweak on existing methods" or something along those lines.

[felippee]

No, there is something special to machine learning and AI in general. E.g. SIGGRAPH papera are different. No one there claims to solve more than they actually do solve. DL is soaked with hype and self congratulatory BS. The best way to spot it is to check the citations. Typically they solve an already solved problem, skipping entirely any pre deep learning literature on it (or if they do cite it, only to dump BS on it) and then just cite a few of their own more or less relevant papers. I'm aware I'm overgeneralizing here and not every paper is like that, but I've seen enough to detect a trend.

It is as if defending or advertising "deep learning" was the purpose of the paper. It is not. The purpose of a paper is to show a solution to a problem. Much of DL literature (again not all) is a "solution in a desperate search of a problem" rather than the opposite.

I think many of these papers (including this one) would make a great blog post, but just isn't quite enough in terms of scientific content for a full blown paper. A curiosity, nice gimmick, but nothing more. Not really a solution to a problem, not really any idea of non trivial universality.