2 ms·
No mention of GANs? As far as I remember, their images felt much more like "sampled from training data" than "averaged from training data". The idea was that yo
by cousin_it 2mo ago
No mention of GANs? As far as I remember, their images felt much more like "sampled from training data" than "averaged from training data". The idea was that you train a "discriminator" that tells generated images apart from real ones, and make the "generator" try to fool it. But somehow it lost to diffusion models, and now all AI imagery looks like slop, when GAN outputs (though imperfect) didn't look nearly as slop. I don't really know what happened though.
- 56767865678 2mo ago[flagged]
- FeepingCreature 2mo agoIirc it lost to diffusion models because it was extremely hard to train. Due to the two adversarial networks it was very prone to mode collapse, ie. the generator running ahead of the discriminator and destroying the training signal. Or other various fun ways the two networks could blow each other up.