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1/ So a way to translate what you're saying there into an implementation would be to add a "cost" to adding "too much purple" as a numerical penalty. But the big problem with this is that penalizing with this magic number will inevitably have bad "side effects" because there's
2/ no way to accurately weigh this with a "one size fits all" approach. You want the machine to learn how much is too much purple instead, according to nuanced contextual information. This is what I view as the chief advantage of GANs (which I don't use currently).
3/ To elaborate on what those bad side effects would be: The model would just pick another default color. Much like a game of whack-a-mole!
I'm not sure what color space you use but if you go to something like CieLAB, does the default color become more grayish?
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