Hi @Bauke_Brenninkmeijer,
I am not an expert either. Yes, I am using it in my GAN tests. Currently, all of my tests are based on point clouds (astronomy research on galactic dynamics), and it works much better than standard WGAN-GP. With this, I can use 1-1 training schedule and get faster convergence (WGAN-GP, 1-1 ratio of G,D training, OMD-Adam, b1=0, b2=0.9).
Now, “works much better”: means I get convergence faster into something that looks OK (for point clouds). But if I want to get the real distribution I have to leave it train for a long time (12-24h). But many more tests are needed. E.g. I’ve noticed that it is very easy for GANs to overfit outliers. Also, I am not interested for the points to be spot on to the sample, but to represent the underlying distribution. I do not know to what extent the optimizer is responsible for this. I still have some way to go with this, because it is not in my standard duties at work.
E.g. “looks” good:

CircGaussian_epoch::6100.png1080×432 37.3 KB

if you focus on the middle plot, but if you focus on the left one, I see that the fake and blue points do not have the same dispersion - so some kind of overfitting. I guess …
My results are not conclusive. I can only say it speeds up convergence, and currently is my standard method for GAN tests.
All the best,
Foivos