On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling
CoRR(2024)
摘要
We study the problem of training diffusion models to sample from a
distribution with a given unnormalized density or energy function. We benchmark
several diffusion-structured inference methods, including simulation-based
variational approaches and off-policy methods (continuous generative flow
networks). Our results shed light on the relative advantages of existing
algorithms while bringing into question some claims from past work. We also
propose a novel exploration strategy for off-policy methods, based on local
search in the target space with the use of a replay buffer, and show that it
improves the quality of samples on a variety of target distributions. Our code
for the sampling methods and benchmarks studied is made public at
https://github.com/GFNOrg/gfn-diffusion as a base for future work on diffusion
models for amortized inference.
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