Normalizing Flows: An Introduction and Review of Current Methods
It provides a comprehensive overview for researchers and practitioners in machine learning, but is incremental as it synthesizes existing work without introducing new methods.
This survey article reviews the literature on Normalizing Flows, generative models that enable efficient and exact sampling and density evaluation, covering their construction, current state-of-the-art methods, and future research directions.
Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the literature around the construction and use of Normalizing Flows for distribution learning. We aim to provide context and explanation of the models, review current state-of-the-art literature, and identify open questions and promising future directions.