解决生成扩散模型的条件采样难题，提升在逆问题中的应用能力
Conditional sampling within generative diffusion models
- 基于联合分布或已有边缘分布构建条件采样方法
- 提出系统性框架整合现有条件采样技术
- 适用于贝叶斯反问题等需要条件生成的场景
生成扩散模型是一类强大的蒙特卡洛采样器，利用桥接马尔可夫过程近似复杂高维分布，如图像处理和语言模型中的分布。尽管在这些领域取得成功，一个重要开放挑战仍存在：将这些技术扩展到条件分布采样，例如在贝叶斯反问题中所需。本文对生成扩散模型中的条件采样现有计算方法进行了全面综述，重点强调了两类关键方法：一类利用联合分布，另一类依赖（预训练）边缘分布及其显式似然，以构建条件生成采样器。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。