> The most famous problem of GANs is instability during training and mode collapse - which is like a student learning especially for an exam (and not in general) thus optimising for the test instead of the real thing.

I must confess I haven't worker with GANs yet, but isn't that the whole point of GANs? Student is optimising for the test while the teacher is learning how to make tests as similar to reality as possible?

If I understand correctly, the main challenge is finding a way to allow teacher and student (well, generator and adversary) to learn at a similar rate, so that one doesn't stop learning because its competitor is too advanced. Is that correct?