Generative Diffusion From An Action Principle
Akhil Premkumar
Generative diffusion models synthesize new samples by reversing a diffusive
process that converts a given data set to generic noise. This is accomplished
by training a neural network to match the gradient of the log of the
probability distribution of a given data set, also called the score. By casting
reverse diffusion as an optimal control problem, we show that score matching
can be derived from an action principle, like the ones commonly used in
physics. We use this insight to demonstrate the connection between different
classes of diffusion models.