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.

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).