Hey @sliceofcheese ,
Thanks for stepping in!
I ported the project code to Pytorch 0.4 recently (the code is available on the master branch of the same repo). I don’t plan to improve it in any way anytime soon as it really takes a lot of time (took me one month and half full time for this one). And yeah I know Let’s enhance too and their results are much better than mine but the difference is, as you said, I offer mine for free while they don’t but at the same time I cannot afford to spend so much time improving it.
Still, there is a lot of stuff to add on top of this implementation to make it better such as adding and removing haze or exploring the E-GAN paper and replacing vanilla GANs from my current implementation by this one.
(I copy the content of the E-GAN paper post I made on a private part of the forums here for those who cannot access it on the http link)
A few weeks ago a new paper came out called E-GAN6 with a video12 accompanying it. I’ve read the paper and from what I could tell this seems to be far better than the existing GAN techniques/architectures. The LSUN bedrooms results look stunning and the promises of this paper are that with the help of evolutionary algorithms the issues inherent to GANs such as mode collapse, vanishing gradient, hyperparameters tunning and difficulty to measure the effectiveness of the network during training belongs to the past.
I personally experienced few of these issues myself when I made my own implementation of a super-resolution paper1 (“run and play” code here3, blog post here7 and demo here1 ). I had to tweak a lot of things, restart 1 week of training from scratch, find tricks to make the nash equilibrium stable etc…
Don’t forget that any PR is welcome 