I really would like to see them try different learning sets that vary the "styles" of retouching. This example looks like it's strongly biased to the "make the images pop!" style of retouching, blowing highlights, shadows and contrasts.

What if the input set has more subtle retouching that pulls highlights and pushes shadows, but without the aforementioned issues?

What if they got their hands on the unedited and edited magnum photos? That would produce an interesting B&W filter, for sure!

https://www.slrlounge.com/magnum-photos-darkroom-magic-genes...

I wonder how many images are required to train a network like this?

If it's in the millions, getting pre and post retouching image pairs in such a quantity is likely impractical.

Ah, right, that could also be a limitation.

Although I'm pretty sure Magnum Photos has a large quantity of images, but perhaps not all in a consistent style.

What exactly is awesome about it? What are they actually achieving that is impressive.

This is basically doing something at low resolution and applying the transformation to the high resolution image using a bilateral filter to make the interpolation respect edges. There isn't really anything new here except for the combination of buzzwords in the title.