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Also, here's a writeup on how the machine learning system works: https://huemint.com/about/#machine-learning https://huemint.com/about/#machine-learning You ca

Also, here's a writeup on how the machine learning system works: https://huemint.com/about/#machine-learning https://huemint.com/about/#machine-learning You can guide the ML model by locking one or more colors, then clicking generate again. (click the circular swatch on top to lock a color)

isoprophlex 5y ago
This writeup is fantastic. Clear description, beautiful visualizations... looks like a true labor of love.
SpaceManNabs 5y ago
This should be added to the AI for art list going around! edit: I forgot where I saw the list for 2021 but it included stuff like this https://twitter.com/ML4CDworkshop/status/1467661464400183298?t=Oy8aXVSvHwOmXfZMXH0vUQ&s=09 https://twitter.com/ML4CDworkshop/status/1467661464400183298...
tqi 5y ago
This was really cool project and a great writeup! It's interesting to think what would happen if a tool / technique like this one became super popular. Would we see more variation, as more people could choose schemes that look good? Or would we see convergence as there are no longer people actively making decisions (similar to what may be happening in the stock market due to the rise of index funds: [1])? [1] https://www.theatlantic.com/ideas/archive/2021/04/the-autopilot-economy/618497/ https://www.theatlantic.com/ideas/archive/2021/04/the-autopi...
jerpint 5y ago
Very impressive technical work, a relatively complex approach explained in a simple way, well done
runeks 5y ago
Excellent explanation! I came here thinking “why use ML for this?” and this is specifically addressed: > You might ask at this point, if we can enumerate all possible solutions like this, why not just generate colors from first principles - why is machine learning even needed? The answer is basically that translation and rotation in colorspace might be contrast-invariant, but they're not preference invariant. Humans have subjective preferences for certain color combinations over others, so to generate pleasing color combinations we need to quantify which areas in the configuration space people generally prefer. > To quantify which color combinations graphic designers prefer, I started by scraping design thumbnails from the web. > At this point we have a decent sized dataset to train our ML model. The overall approach is to treat the problem as conditional image generation - the color contrast graph is the input and the corresponding color palette is the output.

isoprophlex 5y ago

This writeup is fantastic. Clear description, beautiful visualizations... looks like a true labor of love.

SpaceManNabs 5y ago

This should be added to the AI for art list going around! edit: I forgot where I saw the list for 2021 but it included stuff like this https://twitter.com/ML4CDworkshop/status/1467661464400183298?t=Oy8aXVSvHwOmXfZMXH0vUQ&s=09 https://twitter.com/ML4CDworkshop/status/1467661464400183298...

tqi 5y ago

This was really cool project and a great writeup! It's interesting to think what would happen if a tool / technique like this one became super popular. Would we see more variation, as more people could choose schemes that look good? Or would we see convergence as there are no longer people actively making decisions (similar to what may be happening in the stock market due to the rise of index funds: [1])? [1] https://www.theatlantic.com/ideas/archive/2021/04/the-autopilot-economy/618497/ https://www.theatlantic.com/ideas/archive/2021/04/the-autopi...

jerpint 5y ago

Very impressive technical work, a relatively complex approach explained in a simple way, well done

runeks 5y ago

Excellent explanation! I came here thinking “why use ML for this?” and this is specifically addressed: > You might ask at this point, if we can enumerate all possible solutions like this, why not just generate colors from first principles - why is machine learning even needed? The answer is basically that translation and rotation in colorspace might be contrast-invariant, but they're not preference invariant. Humans have subjective preferences for certain color combinations over others, so to generate pleasing color combinations we need to quantify which areas in the configuration space people generally prefer. > To quantify which color combinations graphic designers prefer, I started by scraping design thumbnails from the web. > At this point we have a decent sized dataset to train our ML model. The overall approach is to treat the problem as conditional image generation - the color contrast graph is the input and the corresponding color palette is the output.