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：对于寻求将强大机器学习方法应用至特定问题的实践者而言，采样方法的数量可能令人望而生畏。本文采取理论视角，回顾并梳理生成建模场景下的多种采样方法——在该场景中，目标在于生成与某些训练样本相似的新数据。通过揭示现有方法之间的关联，本文或有助于攻克当前扩散模型采样中的若干挑战，例如因扩散模拟导致的过长推理时间，或生成样本缺乏多样性的问题。