Conditional sampling within generative diffusion models
<|reference_start|>Conditional sampling within generative diffusion models: 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.<|reference_end|>
@article{zhao2024conditional,
    title={Conditional sampling within generative diffusion models},
    author={Zheng Zhao and Ziwei Luo and Jens Sj"olund and Thomas B. Sch"on},
    journal={arXiv preprint arXiv:2409.09650},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09650},
    primaryClass={stat.ML cs.LG}
}