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.
Here’s how it works: First, the algorithm takes an image from the training set. As before, let’s say that each of the million pixels has some value, and we can plot the image as a dot in million-dimensional space. The algorithm adds some noise to each pixel at every time step, equivalent to the
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