I have implemented a vanilla GAN which gave good results very fast but it had a lot of mode collapse issue, because of this I learned about WGAN which suppose to fix this, in fact they claim they have never encountered mode collapse problem which is great. My main issue is how slow WGAN takes to converge to good results. With a vanilla GAN by epoch 10 I was getting good looking generations, with WGAN I am still seeing noise images on epoch 50! Besides bumping up the learning rate is there anything else I can do? The network is a modified smaller DCGAN that has 1 less output layer than the original. So instead of the extra 1024 output layer mine stops at 512. I knew WGAN was going to be slow from all the research I have done but I didn't except to be this slow. Can some one point me to the right direction on how to better optimize WGAN's speed?
I have googled around but most results are just vague comment chains or agreements that there is a speed problem with no further leads on what you should do next to counter this issue.