Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simple) base distribution and a series of bijective transformations. There has been much recent work on normalizing flows, ranging from improving their expressive power to expanding their application. We believe the field has now matured and is in need of a unified perspective. In this review, we attempt to provide such a perspective by describing flows through the lens of probabilistic modeling and inference. We place special emphasis on the fundamental principles of flow design, and discuss foundational topics such as expressive power and computational trade-offs. We also broaden the conceptual framing of flows by relating them to more general probability transformations. Lastly, we summarize the use of flows for tasks such as generative modeling, approximate inference, and supervised learning.
翻译:正常化流动提供了一个界定明显概率分布的一般机制,仅要求说明(通常简单)基本分布和一系列双向转换。最近就正常流动开展了许多工作,从改善其表达力到扩大应用范围等,包括改进流动的正常化。我们认为,该领域现在已经成熟,需要统一的观点。在这次审查中,我们试图通过概率模型和推理的透镜来描述流动情况来提供这种观点。我们特别强调流动设计的基本原则,并讨论表达力和计算取舍等基本议题。我们还扩大了流动的概念框架,将流动与更普遍的概率转换联系起来。最后,我们总结了流动用于诸如基因化模型、近似推理和受监督的学习等任务的情况。