[tasdfqwer0897]

Hmm, I'm not sure what you mean by applicable loss functions?

I'll answer what I think you're asking and you can tell me if I got it wrong:

There's been a lot of effort spent on coming up with different loss functions for GANs, but https://arxiv.org/abs/1711.10337 shows that, according to the metrics in problem 5, they don't really improve results compared to the original GAN loss function.

There's something called a Wasserstein GAN https://arxiv.org/abs/1701.07875 that you may have heard about, but IMO the useful thing to take away from that paper is their 'gradient penalty' technique and not the new loss function.