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pilooch t1_isiqaqv wrote

Some of my colleagues and myself are working daily with GANs in industry-grade applications.

My current understanding is that due to explicit supervision, DDPM do not directly apply to unpaired datasets, for which GANs shine. There are a few papers about this though, so this should emerge as well. Bear in mind that in industry, some datasets are unpaired by the problem's nature. DDPM are insanely good as soon as the dataset is paired.

GANs generators are very controllable for inference, including real-time. DDPM will follow, but are not there yet exactly AFAIK.

Another quick observation: GANs are more difficult to train but modern implementations and libraries do exhibit fast and accurate convergence.

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ThomaSinn t1_isis572 wrote

Can you provide an example of an implementation and a library that make gan converge well ? I tried some a couple years ago and it wasn't easy for me so I'm curious

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pilooch t1_isiuf8z wrote

We use https://github.com/jolibrain/joliGAN which is a lib for image2image with additional "semantic" constraints. I.e. when there's a need to conserve labels, physics, anything between the two domains. This lib aggregates and improves on existing works.

If you are looking for more traditional noise -> xxx GANs, go for https://github.com/autonomousvision/projected_gan/. Another recent work is https://github.com/nupurkmr9/vision-aided-gan.

The key element in GAN convergence is the discriminator. Joligan above defaults to multiple discriminators by combining and improving on the works above, ensuring fast early convergence and stability while the semantic constraints narrow the path to relevant modes.

We've found that tranformers as generators have interesting properties on some tasks and converge well with a ViT-based projected discriminator.

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