Conditional sampling within generative diffusion models
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929719801282560 |
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| author | Zhao, Zheng Luo, Ziwei Sjölund, Jens Schön, Thomas B. |
| author_facet | Zhao, Zheng Luo, Ziwei Sjölund, Jens Schön, Thomas B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09650 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conditional sampling within generative diffusion models Zhao, Zheng Luo, Ziwei Sjölund, Jens Schön, Thomas B. Machine Learning 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. |
| title | Conditional sampling within generative diffusion models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.09650 |