An optimal control perspective on diffusion-based generative modeling
Abstract: We establish a connection between stochastic optimal control and generative models based on stochastic differential equations (SDEs), such as recently developed diffusion probabilistic models. In particular, we derive a Hamilton--Jacobi--Bellman equation that governs the evolution of the log-densities of the underlying SDE marginals. This perspective allows to transfer methods from optimal control theory to generative modeling. First, we show that the evidence lower bound is a direct consequence of the well-known verification theorem from control theory. Further, we can formulate diffusion-based generative modeling as a minimization of the Kullback--Leibler divergence between suitable measures in path space. Finally, we develop a novel diffusion-based method for sampling from unnormalized densities -- a problem frequently occurring in statistics and computational sciences. We demonstrate that our time-reversed diffusion sampler (DIS) can outperform other diffusion-based sampling approaches on multiple numerical examples.
Submission Length: Regular submission (no more than 12 pages of main content)
Video: https://youtu.be/wQpQg1xIlBA
Code: https://github.com/juliusberner/sde_sampler
Assigned Action Editor: ~Valentin_De_Bortoli1
Submission Number: 1726
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