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Imagine asking it to generate a picture for "duck wearing a hat on Mars": First, it creates a random 10x10 pixel blurry image and asks a neural net: "Could thi
by drcode 5y ago
Imagine asking it to generate a picture for "duck wearing a hat on Mars":
First, it creates a random 10x10 pixel blurry image and asks a neural net: "Could this be a duck wearing a hat on Mars?" and the neural net replies "No, because all the pictures I've ever seen of Mars have lots of red color in them" so the system tweaks the pixels to make them more red, put some pixels in the center that have a plausible duck color, etc.
After it has a 10x10 image that is a plausible duck on Mars, the system scales the image to 20x20 pixels, and then uses 4 different neural nets on each corner to ask "Does this look like the upper/lower left/right corner of a duck wearing a hat on Mars?" Each neural net is just specialized for one corner of the image.
You keep repeating this with more neural nets until you have a pretty 1000x1000 (or whatever) image.
- refulgentis 5y agoNot the case, though in a handwave-y way, same idea - instead of iteratively scaling, you're iteratively denoising. See here, links out to the Cornell NLP PhD describe in even more detail: https://www.jpohhhh.com/articles/inflection-point-ml-art https://www.jpohhhh.com/articles/inflection-point-ml-art
- drcode 5y agoI was kind of explaining how I picture the process in my head, fully aware that it isn't really possible to do an ELI5 on this stuff, and not really having an understanding of the technical details myself, either.