A Practical Diffusion Path for Sampling

Understand

Diffusion models are state-of-the-art methods in generative modeling when samples from a target probability distribution are available, and can be efficiently sampled, using score matching to estimate score vectors guiding a Langevin process.

• However, in the setting where samples from the target are not available, e.g.
• when this target's density is known up to a normalization constant, the score estimation task is challenging.
• Previous approaches rely on Monte Carlo estimators that are either computationally heavy to implement or sample-inefficient.