While the results certainly are pretty, I don't see how learning has taken place. This cartoon filter still has the same issues as previous attempts, which is:

While the results certainly are pretty, I don't see how learning has taken place. This cartoon filter still has the same issues as previous attempts, which is: - omitting borders that are semantically important but have a low color gradient - not collapsing small areas into lines For the first issue, there's an example image of a picnic on white background. A human cartoonist would most likely draw a full outline around the white spoon, because it is important for conveying the type of object that this is supposed to be. With this algorithm, the spoon gets partially merged with the white background and without the reference photo I would have a hard time identifying it as a spoon. For the second issue, kook at the photo of the Asian girl with patterned skirt. A human cartoonist would most likely observe the regular grid pattern and replace it with thin lines, thereby communicating that all of it is one same thing. This algorithm, on the other hand, treats each tile of the pattern individually, thereby making it look more like a crystal or crumbled foil. I personally also prefer white-box algorithms, but there's no denying that creating a cartoon requires a lot of prior knowledge about which features to retain as important and which features to abstract away. As such, I see the real challenge in somehow producing good saliency training data for millions of images. I mean ideally you would want the 5 year video stream plus eye tracking data of a baby starting to grow up...