I think this paper is showing that you "can" train an auto exposure/white balancing/edit flow algorithm with a DL pipeline, but the results do not necessarily mean it will outperform simple and cheaper auto exposure/white balancing algorithms that's out there. And the flexibility in this approach also allows masking and background removal.

However, most of the examples in the paper in fact shows improvements of exposure and color. If you import those images and tweak 3 or 4 adjustments of clarity, curves, exposure, saturation in Polarr or Lightroom, you will quickly get very close to the result produced by this paper. However, it is still impressive that it could get to an exposure histogram that looks exact like the ground truth.

Maybe someone can benchmark this against the Google photos auto enhance. A lot of people turn the auto-enhance in Google off because it sometimes create unnatural looks for photos, which are tolerable to everyday consumer but for pros it just looks bad.

Lastly, if you look very closely on the input images, some of them appears to be artificially adjusted to show how the model works. (last page, 4th row, fist image, which looks both underexposured and overexposured after damping brightness through post processing), and these input images are not always the type of images you can get from cameras.