Progressive Growing of GANs for Improved Quality, Stability, and Variation · HackerPapers discussions

Progressive Growing of GANs for Improved Quality, Stability, and Variation — mentioned by godelski

Wed, 29 Nov 2023 20:15:52 GMT

> I'm disgusted, but not surprised, that teenage kids are generating CSAM like this. Even before we had diffusion models, we had GANs and deepfakes, which were almost immediately used for generating shittons of nonconsensual porn I think the big difference now is that 1) it's much easier to do now, and 2) the computational requirements and (more importantly) technical skills have dramatically dropped. We should also be explicitly aware that deep fakes are still new. GANs in 2014 were not creating high definition images. They were doing fuzzy black and white 28x28 faces, poorly, and 32x32…

Paper and discussion history. A mention is not an endorsement.

Progressive Growing of GANs for Improved Quality, Stability, and Variation — HN submission

Thu, 08 Nov 2018 16:34:47 GMT

Progressive Growing of GANs for Improved Quality, Stability, and Variation

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Progressive Growing of GANs for Improved Quality, Stability, and Variation — mentioned by NegatioN

Thu, 25 Oct 2018 20:04:35 GMT

I don't know exactly what was used to upscale here, but Progressive Growing of GANs[0] was a breakthrough last year that proved at least 1024x1024 was viably producable. The short explanation is: Train and freeze the most basic layer of the model progressively to "let the network" understand higher resolution concepts one piece at the time, and avoid mode-collapse. The network architecture illustrating this a bit better is shown on page 3 of the paper [1] [0]: https://github.com/tkarras/progressive_growing_of_gans [1]: https://arxiv.org/pdf/1710.10196.pdf

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Progressive Growing of GANs for Improved Quality, Stability, and Variation — mentioned by nl

Fri, 20 Apr 2018 06:23:53 GMT

GANs (which he invented) are a game changer, and have enabled things to be built which I thought were 20 years away. For example, the life-like faces NVidia research created in Progressive Growing of GANs for Improved Quality, Stability, and Variation[1] are just an incredible achievement (it's worth looking at the pics in that paper if you haven't seen them). Approaches like that have real-life impact in things like games, architecture, design, film, etc (and that is just the obvious implications for images). GANs of course can be used outside images and they have just as much potential…

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Progressive Growing of GANs for Improved Quality, Stability, and Variation — mentioned by nealeratzlaff

Tue, 02 Jan 2018 22:53:55 GMT

I think for most people that hurdle of understanding is immense, and isn't likely to get any better. ML/DL infrastructure will continue to improve and deploying AI systems will become more accessible as time goes on. But I don't see familiarity with (non) convex optimization, statistical learning, topology, etc. becoming mainstream. Without these its hard to see a reasonable path forward for AI. For me this year in DL was a lot narrower. it mostly revolved around systems becoming more human in capability. I think most of this is in the article already: We can now hopefully say that ImageNet…

Paper and discussion history. A mention is not an endorsement.