# Normalizing Flows: An Introduction and Review of Current Methods

arXiv: https://arxiv.org/abs/1908.09257 · HTML: https://mlsanity.com/paper/1908.09257/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2019.

## Built on by (most cited first, 16 of 16)

- [IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing](https://mlsanity.com/paper/2301.13359/) (2023)
- [Implicit Diffusion: Efficient Optimization through Stochastic Sampling](https://mlsanity.com/paper/2402.05468/) (2024)
- [Counterfactual (Non-)identifiability of Learned Structural Causal Models](https://mlsanity.com/paper/2301.09031/) (2023)
- [Continuous Latent Process Flows](https://mlsanity.com/paper/2106.15580/) (2021)
- [Model-Based Machine Learning for Communications](https://mlsanity.com/paper/2101.04726/) (2021)
- [Transformer Neural Autoregressive Flows](https://mlsanity.com/paper/2401.01855/) (2024)
- [Robust Classification using Hidden Markov Models and Mixtures of Normalizing Flows](https://mlsanity.com/paper/2102.07284/) (2021)
- [Normalizing Flow based Hidden Markov Models for Classification of Speech Phones with Explainability](https://mlsanity.com/paper/2107.00730/) (2021)
- [Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications](https://mlsanity.com/paper/2307.02881/) (2023)
- [In-game Residential Home Planning via Visual Context-aware Global Relation Learning](https://mlsanity.com/paper/2102.04035/) (2021)
- [Generative Actor-Critic: An Off-policy Algorithm Using the Push-forward Model](https://mlsanity.com/paper/2105.03733/) (2021)
- [Modeling Time-Series and Spatial Data for Recommendations and Other Applications](https://mlsanity.com/paper/2212.13259/) (2022)
- [PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm](https://mlsanity.com/paper/2306.06637/) (2023)
- [Learning and Generalization in Overparameterized Normalizing Flows](https://mlsanity.com/paper/2106.10535/) (2021)
- [Efficient spatio-temporal weather forecasting using U-Net](https://mlsanity.com/paper/2112.06543/) (2021)
- [Learning Robust Representations Of Generative Models Using Set-Based Artificial Fingerprints](https://mlsanity.com/paper/2206.02067/) (2022)

## Abstract (arXiv)

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.