The number of sampling methods could be daunting for a practitioner looking to cast powerful machine learning methods to their specific problem. This paper takes a theoretical stance to review and organize many sampling approaches in the ``generative modeling'' setting, where one wants to generate new data that are similar to some training examples. By revealing links between existing methods, it might prove useful to overcome some of the current challenges in sampling with diffusion models, such as long inference time due to diffusion simulation, or the lack of diversity in generated samples.

翻译：对于希望将强大的机器学习方法应用于特定问题的实践者而言，众多采样方法可能令人望而生畏。本文以理论视角梳理并组织"生成建模"场景下的多种采样方法——该场景旨在生成与训练样本相似的新数据。通过揭示现有方法之间的内在联系，本文或有助于克服当前扩散模型采样中的若干挑战，例如因扩散模拟导致的推理时间过长，以及生成样本多样性不足等问题。