DALL·E 2 can assemble Modern Art concepts into something that might have made Dalí proud. Via QuantaMagazine
Sohl-Dickstein used the principles of diffusion to develop an algorithm for generative modeling. The idea is simple: The algorithm first turns complex images in the training data set into simple noise—akin to going from a blob of ink to diffuse light blue water—and then teaches the system how to reverse the process, turning noise into images.
Do this for all images in the data set, and an initial complex distribution of dots in million-dimensional space turns into a simple, normal distribution of dots around the origin. Yang Song helped come up with a novel technique to generate images by training a network to effectively unscramble noisy images.Next is the machine-learning part: Give a neural network the noisy images obtained from a forward pass and train it to predict the less noisy images that came one step earlier. It’ll make mistakes at first, so you tweak the parameters of the network so it does better.
Sohl-Dickstein recalls the first outputs of his diffusion model. “You’d squint and be like, ‘I think that colored blob looks like a truck,’” he said. “I’d spent so many months of my life staring at different patterns of pixels and trying to see structure that I was like, ‘This is way more structured than I’d ever gotten before.’ I was very excited.”in 2015, but it was still far behind what GANs could do.
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