Conditional sampling within generative diffusion models

• URL: http://arxiv.org/abs/2409.09650v1
• Date: Sun, 15 Sep 2024 07:48:40 GMT
• Title: Conditional sampling within generative diffusion models
• Authors: Zheng Zhao, Ziwei Luo, Jens Sjölund, Thomas B. Schön,
• Abstract summary: We present a review of existing computational approaches to conditional sampling within generative diffusion models. We highlight key methodologies that either utilise the joint distribution, or rely on (pre-trained) marginal distributions with explicit likelihoods.
• Score: 12.608803080528142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative diffusions are a powerful class of Monte Carlo samplers that leverage bridging Markov processes to approximate complex, high-dimensional distributions, such as those found in image processing and language models. Despite their success in these domains, an important open challenge remains: extending these techniques to sample from conditional distributions, as required in, for example, Bayesian inverse problems. In this paper, we present a comprehensive review of existing computational approaches to conditional sampling within generative diffusion models. Specifically, we highlight key methodologies that either utilise the joint distribution, or rely on (pre-trained) marginal distributions with explicit likelihoods, to construct conditional generative samplers.

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