It was a great opportunity to deep dive into GANs (generative adversarial networks), and generate some dogs images! My solution was pretty simple, just DCGAN with a couple of tricks.
Participants discovered interesting techniques to squeeze into time limits and generate plausible pictures with small resources. Check the top solutions, they generate much better results using tuned Big/Style GAN architectures.
Probably this competition is not the most useful in the world, but definitely could help to emerge some advances in GANs training and architectures, I believe. So probably such interesting applications of these networks like GauGAN eventually become less computationally expensive and more accessible for the community of researchers.
Hurrah! Well done! I really enjoyed competing in this challenge too.
The advancements made by competitors in this challenge are really interesting, especially the context of small images, trained on a single GPU for less than 9 hours. A sort of ImageWoof for GANs. The winners all used BigGAN. I hadn’t gone near it since I read it took many days to train. More fool me. I may try to port some of the winning solutions to fastai before I get pulled into another challenge.
Hey, thank you! (BTW, nice work on AI blogging, and a great DL rig).
Oh completely agree! I also easily excluded BigGAN from consideration when reading how many GPU they used to train their model Sure enough, it was a bit too fast conclusion. Also, I pretty quickly discarded conditional GANs as soon as my first attempts showed bad results. Should try more, I believe.
So this idea of adapting complex architectures to simpler tasks is one of the greatest insights taken from this competition, I would say.
(Generative Dog Images competition).
Sure enough, it was a bit too fast conclusion. Also, I pretty quickly discarded conditional GANs as soon as my first attempts showed bad results. Should try more, I believe.