fast-style-transfer

Dunno about you, but when I first saw some of the results, I was amazed. Here are a few more examples: https://github.com/jcjohnson/fast-neural-style https://github.com/lengstrom/fast-style-transfer Admittedly, though, I haven't seen the Photoshop filters you speak of. Could you link to some of them that show these same effects?

It's showing a visualization of all the intermediate activations of the style transfer network. The intermediate pictures are 4D, so they're visualized as a sequence of tiles. The network being run is defined here https://github.com/lengstrom/fast-style-transfer/blob/master... This post provides a pretty good explanation of what's happening: https://shafeentejani.github.io/2017-01-03/fast-style-transf... There's a sequence of 9x9 and 3x3 convolutions that transforms that one big input image into a bunch of smaller images. They're processed by a sequence of residual convolutions. Finally, these tiny tiles are merged together back into a stylized image of the same size as the original input with a few deconvolution operations.

I used the github library here https://github.com/lengstrom/fast-style-transfer to create the video.

To get an idea of how style transfer works, it can be useful to look at some less successful examples. Here's one from the same git repo: https://github.com/lengstrom/fast-style-transfer/blob/master... (Original photo: https://github.com/lengstrom/fast-style-transfer/blob/master... ) With the "Great Wave" painting as the network's style input, the limitations of the technique become more apparent. It's clear that a human painter would never render the Chicago skyline in this way: there are incongruent little waves on buildings' edges and all over the sky. The antennas on top of the tallest tower are particularly revealing. The neural network just sees an area of higher local contrast, and has continued the same pattern that was applied in the sky at the top-right of the antennas but with more contrast applied. This doesn't make any sense