Normalizing Flows: An Introduction and Review of Current Methods
Ivan Kobyzev, Simon J. D. Prince, Marcus A. Brubaker
Abstract
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.
Topics & Concepts
Current (fluid)Computer scienceContext (archaeology)Generative grammarSampling (signal processing)Artificial intelligenceMachine learningData scienceGeographyEngineeringComputer visionElectrical engineeringArchaeologyFilter (signal processing)Generative Adversarial Networks and Image SynthesisGaussian Processes and Bayesian InferenceMachine Learning and Data Classification