I mean they added some web interface widgets, some cool java script and stuff, but is that what Microsoft payed 1B? Reposting 3 year old stuff from Google brain?
I feel _exactly_ the same. And I know many, many other people who feel the same thing too. Apple will eventually run into a wall, like it did before in the 90's. They don't have a clear vision or connection to the actual user. They just milk the cow that Steve Jobs left them.
I'm not sure how to interpret these pictures. They don't suggest anything to me. And certainly don't suggest anything about the quality of representations. And BTW how do you measure quality of representations?
Yes it is certainly not fair that the network they spend one page explaining and probably weeks training and researching can be hardwired in 30 lines of python. This is very unfair. But this is the reality, and so the post states.
Also the idea to add coordinate as a feature has been used in the past without giving even much thought.
Toy examples are great. As long as they are not trivial. Some guy, presumably smart, once said that "things should be as simple as possible but not simpler". The toy example they play with is just too simple.
I highly doubt they spent weeks training on the toy example. More like five minutes, probably. Again, that the weights learned (quickly) for the toy example can also be set by hand is not surprising and is evidence of a good toy example. The paper’s main result is not the toy example, but the real experiments (for which I doubt you could hand-code the network weights).
"For any problem involving pixels or spatial representations, common intuition holds that convolutional neural networks may be appropriate. In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate transform problem, which simply requires learning a mapping between coordinates in (x,y) Cartesian space and one-hot pixel space. Although convolutional networks would seem appropriate for this task, we show that they fail spectacularly. We demonstrate and carefully analyze the failure first on a toy problem, at which point a simple fix becomes obvious."
Author of the post here: I think their paper would have been much better if they included the piece of code which I wrote in python to explain that the transformation they are learning is obviously trivial and the fact that it works is not in question. This would leave them a lot more space to focus on something interesting, perhaps explore the GAN's a little further, cause what they did is somewhat rudimentary. But that omission (and lack of context for previous use of such features in the literature) left a vulnerability which I have the full right to exploit in a blog post.
Author of the post here. I totally agree that negative stuff should be published. But without the fanfares. I think they could have changed the tone of that paper and I would not have an issue with it. It is likely that if they did that they'd never go through some idiot reviewer who expects "a positive result" or some similar silliness. This is not a perfect world. The paper as is makes strong claims about the novelty and usefulness of their gimmick. If it turns out your stuff is at least partially hollow and you take on the pompous tone, you have to be ready to take some heat. Science is not about tapping friends on the back (which BTW is what is happening a lot with the so called "deep learning community"). Science is about fighting to get to some truth, even if that takes some heat. People so fragile that they cannot take criticism should just not do it.
I completely agree regarding fanfare in deep learning. There are lots of “incremental improvement” papers, GitHub repos, blog posts, etc. and these are totally fine in principle — but they are without a doubt branded as “state of the art” often with messy or incomplete code and little capability to reproduce results.
An additional frustration point I always have is when network architectures are not even fully specified.
Try reading the MTCNN fave detection paper. How, exactly, is the input image pyramid calculated? By what mechanism, exactly, can the network cascade produce multiple detections (i.e. can it only produce one detection per each input scale? If more than one, how?). In the Inception paper dealing with factorized convolutions, just google around to see the deep, deep confusion over the exact mechanics by which the two-stage, smaller convolutions ends up saving operations ovrr a one-stage larger convolution. The highest upvoted answers on Stack Overflow, reddit, quora are often wrong.
And these examples are from reasonably interesting mainstream papers that deserve some fanfare. Just imagine how much worse it is for extremely incremental engineering papers trying to milk the hype by claiming state of the art performance.
Still though, at the end of the day, I’d rather that more papers are published and negative / incremental results are not penalized, because the alternative file drawer bias would be much worse for science overall.
The post mocks them primarily for learning the trivial coordinate transform. That is the core of the paper and ridiculing this piece leaves very little left on the table. The ImageNet test is just an appendix, a cherry on the cake, a curiosity one should say.
Author here (of the post, not the paper). I think you don't understand how science works. The whole point of the exercise (which indeed may have been forgotten these days) is to attack ideas/papers. The first line of attack should be your friends to make sure you don't put anything out there that is silly. The second line of attack are the reviewers, who may or may not be idiots themselves, but in the perfect world should serve the same purpose. The third line attack are independent readers, people like me. I found it to be trivial, took my liberty to attack it. It is not personal and should not be taken so. These guys may in the future publish the most amazing piece of research ever. But this one is not it. They should realize this and my blog post serves this purpose. If somebody gets offended and takes it personally, so be it. I think people should have a bit thicker skin, especially in science. I took quite a bit of bullshit myself (and I'm sure I will have to take more) and never complained. So relax, read the paper, read the post, learn something from both and go on.
Don't hide behind the pretense of doing science to justify being a jerk. Look at your own language, in this reply, and in your blog post:
"you don't understand how science works" - this is attacking a person, not an idea.
The blog post:
"Perhaps this would be less shocking, if they'd sat down and instead of jumping straight to Tensorflow, they could realize" [...]
"They apparently have spent to much time staring into the progress bars on their GPU's to realize they are praising something obvious, obvious to the point that it can be constructed by hand in several lines of python code."
This makes assumptions about the authors, and all but calls them idiots. That entire paragraphs drip with sarcasm, of which one can only assume you're smart enough to be aware and have intended. You made it personal, and that's exactly what the GP is noting when they term your blog post a "hit piece".
Yes, people have used explicit coordinates as features before. No, this paper isn't going to radically change the world, but if you're arguing from "science", that _doesn't matter_ at all. Science is full of rediscovery and duplication, and tolerates it just fine. What matters most is that we filter out things that are wrong -- and I don't think that's obviously the case with this paper. "Trivial" is a subjective determination, and while one part of the job of refereeing a journal or conference is to try to rank things as a service to the audience, it's not the most important aspect of a reviewer's job.
Just because you took a lot of bullshit doesn't mean it's OK. It's not OK if people were jerks to you in this way, and it's not OK to pass it on.
Oh, somebody got triggered here! Yes, there is sarcasm in this post! And if you don't like it, fine. But please, don't give me bullshit about being a jerk. I think you probably have not seen a real jerk in your life yet.
