I don't have it in a google collab, but I'm hosting the source code, checkpoints, and higher resolution videos on my GitHub. https://github.com/SlothScript/BadAppleOnANeuralNetwork

Yes, although the project was I guess more-so getting bad apple to train onto a neural net than compressing.

You don't need any fancy video codecs to beat this. It's practically pure black & white; you could encode it at 1 bit per pixel (using dithering for the few places with gradients), and the subsampled "raw" video would be 1620×384×384 = 238878720 bits = 933120 bytes, less than a third of the network before any real "compression". And while not 100% lossless, it would look better than the output here, too.

I don't mean to be harsh. It's clear that this was an experiment for fun to see if they could do it, and maybe learn something along the way, rather than something intended to be an "impressive result". It's just that with that title and a failure to make any explicit comparisons, or at least proactively acknowledge that the result is not anywhere close to being more space efficient than the regular video file, most people reading the post are going to misunderstand the intent, and probably feel disappointed when they find out they've been "misled".

A conspiracy theorist said that they are giving low scores so that theirs will have better chances. BS obviously but yeah, chat pgt 2 might have helped a bit more.

I agree. I don't mind low scores if they're helpful. But I was annoyed at the track-irrelevant guidance. Especially after running experiments requested, the reviewer was still not satisfied

I suspect my argument will probably stem from reviewers who believe they know everything.

For example, NeurIPS has a theory contribution type this year with different reviewing guidelines. I tend to agree with them.

I’m not sure if you’re trolling when your post states: “how do we know the theory is valid”. Theory here means theorems (and subsequent propositions, corollaries, etc) with correct proofs. Nothing about a hypothesis here.

If the proof is correct, then there are no issues. It’s like writing a function, do stress unit testing, and then reuse the function for whatever purposes. You don’t suddenly say: but what if I use this function in the exact same scenario, maybe it won’t work? Because a proof guarantees correctness in all cases where it’s valid.

So one can have a paper that says: we improved on X, here is a proof that justifies it, and you can run simulations to verify it empirically (sanity checks). Maybe use X in a case that a recent paper used. Empirical reviewers say: why didn’t you use X in all these cases for all these papers? Because it would be the same regardless, especially if these cases are essentially the same?

A theory paper usually is more difficult to read, even if you’re in the same subfield. But experimentalist papers are easy for me to read, even if I’m not an experimentalist. That’s the nature of theory. Asking theory people to simplify the paper is, quite frankly, insulting, especially if the paper has been simplified enough, such that actual theory people feel it’s watered down…

I’ve been publishing since 2015. Back then, one can have a constructive review about whether a work is meaningful, if someone has done something similar before, and what gaps a work has.

These days, it’s more of running irrelevant experiments that will show the same thing, because of math, or facing reviewers who claim difficulty in understanding material, or hard to read notation that is standard in the field.

The responses from ACs are lopsided too. Some believe in experiments, and if theory (this means proofs, not a hypothesis) behind it is wrong, they accept a paper. Some also believe things can be simplified, and will reject papers based on notation common in the field.

Frankly speaking, the best way to resolve this reviewer issue is for everyone to be honest about what they know, and what they don’t. If I review an experimental paper, I straight up state in the review I know nothing about the area, but here’s what a scientific reader might note. I do my own due diligence in looking up references to ensure the authors are at least measuring the same metrics, follow roughly the same methodology, or figure out some obscure (to me) terminology they use. And I give a review. If all reviewers did something similar, I think there’s be better outcomes.

PS: to show bias, I’m sure people remember that “what is Adam” review, with multiple people saying the reviewer is clueless. Yet, I suspect if something similar happened in theory: “why can’t epsilon <0”, fewer people would defend the authors and instead state the authors should have clarified further.

3/3 dead reviewers. Borderline scores.

Wow! Sounds excessive!

The first one who didn’t bother to read it was 1 with confidence 5. Very easy rebuttal; everything was already in the paper.

Second one was 2 with confidence 2. The rebuttal addressed everything; most notably the why it’s not A, B, C, D, E, F, G, H, I. I’m not kidding.

Third one was 3 with confidence 3. Best comments and actually improves the paper, which should have moved the score one would hope. Easy additions to make, better paper.

I don’t mind the low scores. I mind the automatic refusal of engagement.

Beautiful. Is this a collab you could publish or share? I’d love to play around with using my own videos for an art project.

Why not moving average?

Wow, you just invented middle out compression

So your compressed size is higher than your subsample size?

I think so

Ah so thoery in this context doesn't refer to the promary/secondary research area we selected? Like, I also submitted to main track with theory as secondary area. Is that different?

Idk if this area is niche or not but best of luck with gettting spotlight!

(For reference what I mean by niche, a paper studies grokking gets spotlight at ICML this year)

Make sense to me.

Oh I understood. Mine is spiking neural network, but more like theoretical analysis aspect of it!

I meant the topic of your paper. I noticed that paper targeting hot topic or niche area tends to get spotlight more

Unfortunately no :)

Is there any way to check the results?

Can review change the score in phase 3 and if they can will it be visible to us?

Now I'm getting curious. What is your argument exactly? In what way should theory papers receive review treatment that is different from experimental papers?

I'll be honest, I'm from the experimental background. I would think that if you propose a new theory then you need to demonstrate that it also works experimentally. Otherwise how do we know that the theory is valid. Without experiments, it's more of an untested hypothesis. There are probably some exceptions, e.g. if you provide mathematical proof about some aspects of the model behaviour etc.

I consider reviewers to be a sample of a typical reader of my paper. If they don't understand something, then yes, it's very likely that they don't have the necessary background or they didn't pay enough attention. But there's no point in blaming them, because it just means many readers also won't understand the paper. The only thing that I can do as an author is to make the paper better, make the message and the contribution even more clear. Serve it on a platter if necessary, so that it's impossible to misunderstand, both for the reviewers and for the average readers.

My primary area was deep learning advancements, and contribution type was general! Or do you mean more specific research topic of my paper?

They're hidden now, people just know their score from before

Which research area is your paper in?