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The Math Behind GANs (2020)
- colesantiago 1y agoAren't GANs like ancient? Last time I used a GAN was in 2015, still interesting to see a post about GANs now and then.
- GaggiX 1y agoAdversarial loss is still use on most image generators, diffusion/autoregressive models work on a latent space (they don't have to, but it would incredibly inefficient) created by an autoencoder, these autoencoders are trained on several losses, usually L1/L2, LPIPS and adversarial.
- radarsat1 1y agoWhenever someone says this I like to point out that they are very often used to train the VAE and VQVAE models that LDM models use. Slowly diffusion is encroaching on its territory with 1-step models, however, and there are now alternative methods to generate rich latent spaces and decoders too, so this is changing, but I'd say up until last year most of the image generators still used an adversarial objective for the encoder-decoder training. This year, not sure..
- lukeinator42 1y agothey're also used a lot for training current TTS and audio codec models to output speech that sounds realistic.
- pilooch 1y agoExactly, for real time applications VTO, simulators,...), i.e. 60+FPS, diffusion can't be used efficiently. The gap is still there afaik. One lead has been to distill DPM into GANs, not sure this works for GANs that are small enough for real time.
- godelski 1y agoI mean it is really hard to push diffusion models down in size so that just makes the speed part hard. I'm not sure diffusion can ever truly win in the speed race, at least without additional context like breadth of generation. But isn't that the thing? The best model is only the best in a given context? I think the weirdest thing in ML has always been acting like there's an objectively better model and no context is needed.
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- black_puppydog 1y agoYeah, title needs (2020) added. GANs were fun though. :)
- sylos 1y agoThe article is from 2020, so it would be closer to relevancy back then.
- aDyslecticCrow 1y agoGAN is not a architecture its a training method. As the models themselves change underneath, GAN remain relevant. (Just as you see autoencoder still being used as a term in new published works, which is even older.) Though if you can rephrase the problem into a diffusion it seems to be prefered these days. (Less prone to mode collapse) Gan is famously used for generative usecases, but has wide uses for creating useful latent spaces with limited data, and show up in few-shot-learning-papers. (Im actually not that up to speed on the state of art in few-shot so mabie they have something clever that replace it)
- gchadwick 1y agoWhilst it's maybe not worth studying them in detail I'd say being aware of their existence and roughly how they work is still useful. Seeing the many varied ways people have done things with neural networks can be useful inspiration for your own ideas and perhaps the ideas and techniques behind GANs will find a new life or a new purpose. Yes you can just concentrate on the latest models but if you want a better grounding in the field some understanding of the past is important. In particular reusing ideas from the past in a new way and/or with better software/hardware/datasets is a common source of new developments!
- programjames 1y agoThey're used as a small regularization term in image/audio decoders. But GANs have a different learning dynamic (Z6 rather than Z1 or Z2) which makes them pretty unstable to train unless you're using something like Bayesian neural networks, so they fell out of favor for the entire image generation process.
- mindcrime 1y agoTuring machines are ancient as well.
- staticelf 1y agoReading an article like this makes me realize I am too stupid to ever build a foundation model from scratch.
- reactordev 1y agoHaha, I was just going to say the same. I was hoping, I guess naively, that this would explain the math. Not just show me math. While I love a good figure, I like pseudocode just as much :)
- hoppp 1y agoIt takes a while to get into, just like with everything determination is key Also there are libraries that abstract away most if not all the things, so you don't have to know everything
- staticelf 1y agoThat's the thing, it's too hard to learn so I rather do something else with the limited time I have left.
- gregorygoc 1y agoI come from a country which had a strong Soviet influence, and in school basically we were taught that behind every hard formula lies an intuitive explanation. As otherwise, there’s no way to come up with the formula in the first place. This statement is not true, there are counter examples I encountered in my university studies but I would say that intuition will get you very far. Einstein was able to come up with special theory of relativity by just manipulating mental models after all. Only when he tried to generalize it, that’s when he hit the limit of the claim I learned in school. That being said after abandoning intuition, relying on pure mathematical reasoning drives you to a desired place and from there you usually can reason about the theorem in an intuitive way again. Math in this paper is not that hard to learn, you just need someone to present you the key idea.
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- ilzmastr 1y agoIf you ever wondered about the generalization to multiple classes, there is a reason that the gans look totally different: https://proceedings.mlr.press/v137/kavalerov20a/kavalerov20a.pdf https://proceedings.mlr.press/v137/kavalerov20a/kavalerov20a... It turns out 2 classes is special. Better to add the classes as side information rather than try to make it part of the main objective.