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My desktop background is the output of a neural network with random parameters that maps (x, y) to (r, g, b): https://i.imgur.com/KySV77c.jpg.

Here's a small album of other wallpapers I've made like this: https://imgur.com/a/BrNhwkF




"This post may contain adult or erotic images...". Well I didn't knew neural network were in the nsfw categories now.


Related.../u/gabrielgoh in 2016 turned Yahoo's nsfw neural network into a weird abstract image generator. The gitlab repo is private now but you can read some of the comments https://www.reddit.com/r/programming/comments/58k3sf/image_s...

https://news.ycombinator.com/item?id=12756462

Here's a blog post on it, and a warning, it's perceptibly NSFW

https://www.gwern.net/docs/ai/2016-goh-opennsfw.html


> The gitlab repo is private now

The internet always remembers:

https://archive.is/20181205151107/https://open_nsfw.gitlab.i...

https://archive.is/20170506185728/https://open_nsfw.gitlab.i...

(But seriously, that stuff is NSFL. I want to bleach my eyes now. A lot of those are like erotic lovecraftian fantasy landscapes.)


Looks great but I'm surprised they aren't way noisier. Is that just a result of having a limited number of neurons compared to the number of pixels, or is there some specific trick to it?


I think at least some of the structure is down to the update equation, roughly 'new = sign(gate * old + ...)'. Since 'gate' is always positive, this means that even when the network is randomly initialized, the state is more likely to stay the same between two time steps than to change.

I tried "deep transitions" which repeats this equation a couple of times, and the results were indeed noisier.


It's mostly that these are very narrow and shallow networks -- if I remember right, 5 layers and 30 units wide?


Not OP, but I imagine you can set maximum limits on the rate change of the RGB vector so it doesn't create a static but a more fluidic appearing image.




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