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If you use perceptual loss in GANs, it still doesn't get better results than the method you are using now?
That's what I was originally doing actually- perceptual loss along with GAN loss. GANs tend to go haywire with glitches and introduce undesirable constraints. It's a net negative even when paired with the latest training we're doing now (I've tried).
I have been using Deoldify as a reference for my own project (not related to colorization). Might want to reconsider the use of GANs. But I wonder if it is just perceptual loss that leads to such amazing results. If I were to implement it now, no way it's going to be that good.
There's more to it. I'd start by referencing fastai's work on this. There's details I haven't covered here that will be a helpful start.
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