loss functions: Numerous losses have been proposed over the years for training GANs, promising improved performance and stability. I have implemented 5 of them in this repository, and my experience is in line with this GAN study: no loss seems to consistently outperform the default non-saturating GAN loss. I recommend using that as a default.