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So, smoothing and applying a brownish/pink skin color.



What would be really interesting to see in these kinds of articles is how well you could do with just a batch Photoshop operation hand-tuned on a handful of photos, and then a comparison of how much better the NN does.


Isn't that exactly what he did?

I'm assuming he didn't actually take those photos with a gameboy because he has the full color versions of the shots as well.


No, he generated "fake" Gameboy Camera images from the full color shots, by applying a crude form of dithering to graytone. I think that's perfectly reasonable.

What I'm saying is I would like to see comparison with a fourth image produced from the first (black and white) image by a batch Photoshop operation. I would guess a suitably tuned application of "brown tint, then selective Gaussian blur, then unsharp mask" would get you pretty close.


That's pretty much what I thought. I think the concept was pretty neat, and it does a pretty job of smoothing the gradients while maintaining the detail, and I think that's probably the hardest part.

It would be hard for it to accurately get skin color, since the camera is only seeing skin, and it all gets leveled to a similar lightness. So, I'm not surprised that skin color was hard, but the report still probably should not have included the line "Note that even skincolor is accurate most of the times" -- I guess "most" could almost be considered accurate, if the majority of the samples had the same pinkish skin to start with. Almost all the results from the celebrity dataset ended up with the exact same tone, despite wildly different input tones: http://imgur.com/a/daJUa


The model clearly overfits if to compare the fidelity of pictures from the training set vs the out of sample ones (sent in by others).




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