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The difficulty of evaluating GANs is nowhere near the level of unfalsifiability, and it is not caused by GANs themselves being a bad technique, but by the problem space they are applied in.

When you are trying to generate "realistic" samples of human concepts, the ultimate measure of evaluation is whether humans think that the output is realistic. So you have no choice but to ask humans to judge the quality of your results. That's a standard thing to do e.g. in text-to-speech generation, whether GANs are used or not.




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