Why did you not initially upload to arXiv? Just curious

Look at the CVPR policy on publicizing: https://cvpr.thecvf.com/Conferences/2023/AuthorGuidelines

apparently nowadays you can't do PR on it (so don't tweet, I take it back) but surely you can tell your friends and colleagues about the preprint. Once the paper gets accepted or rejected you can start talking about the preprint more publically

Neural magic does work in this space, not sure about KD specifically

Yeah see my problem is that I submitted to cvpr and that’s it. I never put up the preprint on Arxiv which was a mistake in hindsight. I thought if something is in the review cycle, I should keep it there till that finished at least.

Oh I know and I agree, still slightly deflating when it happens though haha!

I have an earlier blog post which is intended precisely to build intuition about diffusion :) https://benanne.github.io/2022/01/31/diffusion.html

Is that ok given it’s still in the review stage?

I just submitted it to Arxiv today once I saw theirs

They do indeed. They prove that the method scales up well with lots of training, which I couldn’t demonstrate in the preprint paper.

As I understand it, the main motivation for latent diffusion is that in perceptual domains, ~99% of information content in the input signals is less perceptually relevant, so it does not make sense to spend a lot of model capacity on it (lossy image compression methods like JPEG are based on the same observation). Training an autoencoder first to get rid of the majority of this irrelevant information can greatly simplify the generative modelling problem at almost no cost to fidelity.

This idea was originally used with great success to adapt autoregressive models to perceptual domains. Autoregression in pixel space (e.g. PixelRNN, PixelCNN) or amplitude space for audio (e.g. WaveNet, SampleRNN) does work, but it doesn't scale very well. Things work much better if you first use VQ-VAE (or even better, VQGAN) to compress the input signals, and then apply autoregression in its latent space.

The same is true for diffusion models, though in this case there is another mechanism we can use to reduce the influence of perceptually irrelevant information: changing the relative weighting of the noise levels during training, to downweight high-frequency components. Diffusion models actually do this out of the box when compared to likelihood-based models, which is why I believe they have completely taken over generative modelling of perceptual signals (as I discuss in the blog post).

But despite the availability of this reweighting mechanism, the latent approach can still provide further efficiency benefits. Stable Diffusion is testament to this: I believe the only reason they are able to offer up a model that generates high-res content on a single consumer GPU, is because of the adversarial autoencoder they use to get rid of all the imperceptible fine-grained details first.

I think this synergy between adversarial models (for low-level detail) and likelihood- or diffusion-based models (for structure and content) is still underutilised. There's a little bit more discussion about this in section 6 of my blog post on typicality: https://benanne.github.io/2020/09/01/typicality.html#right-level (though this largely predates the rise of diffusion models)

Didn’t realise I could do that! Would I just email them with a copy of the preprint, explain I had submitted it back in November, and see would they be able to cite it?

Is that still possible even if my paper gets rejected?

Sorry if these are dumb questions haha, I’m just not sure what best practices are in this type of situation.

Edit: I should add I never initially uploaded my preprint to Arxiv. Only sent it to cvpr. Just uploaded it today though instead.

One thing you can do is that you can upload it to arxiv and mention that this paper was submitted to CVPR in the comments. Best of luck, man. I'm happy that you found a novel method with good results.

You don't have to be worried. At worst, your will be considered as a parallel advancement. Plus, you don't have to be worried about the reviewers for this. Seriously, don't worry.

I understand the deflation feeling, but put your papers up on arxiv asap if it's not already there, and publicize it. Tell your friends, tweet, etc. Comment how it's similar to the big labs and how you are excited about the field moving to this idea, etc.

Great minds think alike and all that!

Why not give us links to the papers?

I'm going to try watching this. Will big co take all the credit? Very interesting

I have a blog post about this here: https://benanne.github.io/2014/04/05/galaxy-zoo.html

The code is here: https://github.com/benanne/kaggle-galaxies ... but it's 8 years old at this point, so getting this to run today could be a bit of a challenge!

My blog posts are mostly shower thoughts expanded into long form, so naturally they tend to be a bit speculative. I have in fact tried a bunch of stuff in the diffusion language modelling space, which culminated in the CDCD paper: https://arxiv.org/abs/2211.15089 as well as this theoretical note on simplex diffusion: https://arxiv.org/abs/2210.14784 -- if the style of the blog post isn't your cup of tea, this might be more to your liking :)

Completely agree re: hard numbers, by the way (I spent quite a bit of time Kaggling during my PhD, see some of my earlier blog posts), but a single researcher can only do so many experiments. Part of the motivation for writing these blog posts is to draw attention to areas of research I think are interesting, and hopefully encourage some people to delve deeper into them as well! Pointing out open questions can be quite conducive to that, in my experience.

I am in a different field and don't use arXiv, but since you submitted your paper prior to the other paper being "published" in arXiv I don't see the problem.

If it gets accepted, you may have to cite the arXiv paper. Assuming people actually cite those papers? I have no idea if that is common practice in your field.

If it gets rejected, just move forward with a lower tier journal. Although, I am not sure how editors evaluate non-peer reviewed self-published papers in terms of "prior art". If you submitted to arXiv prior to submitting it to CVPR, this obviously won't be an issue.

As with any startup ideas, there are usually multiple teams working on it at the same time. I always through my ideas were unique - well, they aren't.

If theirs came out on arXiv just 2 days ago then isn't your preprint still first? If so, you can email to ask the well known research group to cite your preprint in the intro of their work when it comes out (even if their reviewed version comes out before your reviewed version). Nice way to make a connection, too: 'great minds think alike' and all.

Is your paper on arxiv aswell?

I want to increase the resolution of the output images. I want to implement the progressive growing of GANs approach with a pre-trained model, but, it is complicated since the pre-trained model contains (2 encoders "one for faces and one for voice", decoder, and a discriminator).

How should I update the actual architecture/code to reach my goal?

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PS: I saw medium posts about this topic but it isn't the case cuz they are creating their generator/discriminator from scratch.

Don't the new results simply make your paper more impactful?

I dont understand the fact that I have to perform preprocessing and feature selection on my training data set only as to prevent data leakage but when I try to use my classifiers in python they want equal dimensions of my train and validation sets. of course they are not anymore if I only preprocess the training set??? What do i have to do.