Recently I learned how neural style transfer works. I wanted to be able to play with it more and gain some insights, so I adapted the Coursera notebook code to something that works on localhost (more on that in a later post), found myself a nice historical cat image via DPLA, and started mashing it up with all manner of images of varying styles culled from DPLA’s list of primary source sets. (It really helped me that these display images were already curated for looking cool, and cropped to uniform size!)
Let’s get started, shall we?
I really love how this one turned out. It’s pulled the blue and yellow colors, and the concerned face of the lower kitten was a perfect match for the expression on the right-hand muckraker. The lines of the card have taken on the precise quality of those in the cartoon — strong outlines and textured interiors. “Merry Christmas” the bird waves, like an eager newsboy.
This is one of the first ones I made, and I was delighted by how it learned the square-iness of its style image. Everything is more snapped to a grid. The colors are bolder, too, cueing off of that dominant yellow. The Christmas banner remains almost readable and somehow heraldic.
How about Christmas of Steel? These kittens have broadly retained their shape (perhaps as the figures in the comic book foreground have organic detail?), but the background holly is more polygon-esque. The colors have been nudged toward primary, and the static of the background has taken on a swirl of dynamic motion lines.
How about starting with something boldly colored and almost abstract? Why look: the kittens have learned a world of black and white and blue, with the background transformed into that stippled texture it picked up from the hair. The holly has gone more colorblocky and the lines bolder.
This one learned its style so aptly that I couldn’t actually tell where the boundary between the second and third images was when I was placing that equals sign. The soft pencil lines, the vertical textures of shadows and jail bars, the fact that all the colors in the world are black and white and orange (the latter mostly in the middle) — these kittens are positively melting before the force of Wilsonian propaganda. Imagine them in the Hall of Mirrors, drowning in gold and reflecting back at you dozens of times, for full nightmare effect.
Shall we step back a few decades to something slightly more calming? These kittens have learned to take on soft lines and swathes of pale pink. The holly is perfectly happy to conform itself to the texture of these New England trees. The dark space behind the kittens wonders if, perhaps, it is meant to be lapels.
And now for kittens from the void.
Brown, it has learned. The world is brown. The space behind the kittens is brown. Those dark stripes were helpfully already brown. The eyes were brown. Perhaps they can be the same brown, a hole dropped through kitten-space.
I thought this was honestly pretty creepy, and I wondered if rerunning the process with different layer weights might help. Each layer of the neural net notices different sorts of things about its image; it starts with simpler things (colors, straight lines), moves through compositions of those (textures, basic shapes), and builds its way up to entire features (faces). The style transfer algorithm looks at each of those layers and applies some of its knowledge to the generated image. So I thought, what if I change the weights? The initial algorithm weights each of five layers equally; I reran it weighted toward the middle layers and entirely ignoring the first layer, in hopes that it would learn a little less about gaping voids of brown.
This worked! There’s still a lot of brown, but the kitten’s eye is at least separate from its facial markings. My daughter was also delighted by how both of these images want to be letters; there are lots of letter-ish shapes strewn throughout, particularly on the horizontal line that used to be the edge of a planter, between the lower cat and the demon holly.
So there you go, internet; some Christmas cards from the nightmare realm. May 2021 bring fewer nightmares to us all.