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You mention possible score functions in Problem 5, so I have a query about that. This has been bugging me for a bit-- is there a characterization of applicable loss functions for GANs? I'm curious about their effects on the results, but also if there's room for different losses. Or do we capture most desirable behavior using the listed score functions?

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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.

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