neural-doodle

As I mentioned in another thread, there is some work on this: https://github.com/alexjc/neural-doodle

It has been done with manual segmentation [1]. And the results are mind blowing. There also a lot of work done on segmentation with neural nets [2, 3], so I wouldn't be surprised to see someone implementing this idea in the near future. 1: https://github.com/alexjc/neural-doodle 2: https://arxiv.org/pdf/1605.06211.pdf 3: http://mi.eng.cam.ac.uk/projects/segnet/

The image generation portion reminded me of Neural Doodle [1], an impressive project that translates very simple sketches into realistic representations using what it calls 'style transfer'. The page has some great examples like Impressionist paintings and texture creation. [1] https://github.com/alexjc/neural-doodle GIF: https://github.com/alexjc/neural-doodle/raw/master/docs/Work...

The dependency hell required to run a good deep learning machine is one of the reasons why using Docker/VM is not a bad idea. Even if you follow the instructions in the OP to the letter, you can still run into issues where a) an unexpected interaction with permissions/other package versions causes the build to fail and b) building all the packages can take an hour+ to do even on a good computer. The Neural Doodle tool (https://github.com/alexjc/neural-doodle), which appeared on HN a couple months ago (https://news.ycombinator.com/item?id=11257566), is very difficult to set up without errors. Meanwhile, the included Docker container (for the CPU implementation) can get things running immediately after a 311MB download, even on Windows which otherwise gets fussy with machine learning libraries. (I haven't played with the GPU container yet,

These are really cool. Though if you were, like me, puzzled how could some really complex and coherent features come from those simple drawings / masks, have a look at the original paintings that were used as sources and compare them with generated images: Original #1: https://github.com/alexjc/neural-doodle/blob/master/samples/... Generated #1: https://github.com/alexjc/neural-doodle/blob/master/docs/Coa... Original #2: https://github.com/alexjc/neural-doodle/blob/master/samples/... Generated #2: https://github.com/alexjc/neural-doodle/blob/master/docs/Lan... So those new generated images are structurally very similar to the original sources. Neural net seems to be good at "reshuffling" of the sources. That's probably how things like reflections on the water got there, even if not present in the doodles.

There's an implementation of the algorithm on GitHub: https://github.com/alexjc/neural-doodle