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Back to GANs: Because of the extra manual labor involved noted above, they severely reduce your ability to iterate, honestly compare experiments, and narrow down cause/effect. e.g. Is it really an improvement or just that you picked a particularly good stopping point? 4/
So really a big part of getting a good model up is setting up the conditions to be able to iterate through the vast knob tuning landscape, quickly and accurately. Choice of approach (GAN versus not using a GAN) impacts this greatly! 5/
GAN vs not using a GAN is too ambiguous
GANs have some unique instability issues compared to any other option I can think of. That's what I'm trying to point out here.
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