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In case you are wondering about this, you don't need GANs because there is a ground truth. You are just converting noisy images into denoised images. There is literature on "image restoration" showing hybrid L1/perceptual loss do best (http://arxiv.org/pdf/1511.08861 ).https://twitter.com/citnaj/status/1272759311639310336 …
That's exactly it. I found they were learning the same exact things, except GANs were a pain in the butt. So it just made sense to ditch the GAN.
It would be great if such general insights were listed somewhere in a curated fashion. I'm left to assume many AI practitioners are regularly reinventing the wheel. The pace of change is so fast its a challenge to keep up and not use out dated tech from last year, if it seems ok.
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