Large scale GAN training for high fidelity natural image synthesis
I was drawn to this paper to try and find out what’s behind the stunning rate of progress. The large-scale GANs (can I say LS-GAN?) trained here set a new state-of-the-art in class-conditional image synthesis.
So what’s the secret to LS-GANs success? Partly of course it’s just a result of scaling up the models – but interestingly by going wide rather than deep. However, GANs were already notoriously difficult to train (‘unstable’), and scaling things up magnifies the training issues too. So the other part of the innovation here is figuring out how to maintain stability at scale. It’s less one big silver bullet, and more a clever aggregation of techniques from the deep learning parts bin. All in all, it has the feel to me of reaching the upper slopes of an ‘s’-curve such that we might need something new to get us onto the next curve. But hey, with the amazing rates of progress we’ve been seeing I could well be wrong about that.