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A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow,^[1]^[2]^[3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.

The direct modeling of likelihood provides many advantages. For example, the negative log-likelihood can be directly computed and minimized as the loss function. Additionally, novel samples can be generated by sampling from the initial distribution, and applying the flow transformation.

In contrast, many alternative generative modeling methods such as variational autoencoder (VAE) and generative adversarial network do not explicitly represent the likelihood function.

^ Tabak, Esteban G.; Vanden-Eijnden, Eric (2010). "Density estimation by dual ascent of the log-likelihood". Communications in Mathematical Sciences. 8 (1): 217–233. doi:10.4310/CMS.2010.v8.n1.a11.
^ Tabak, Esteban G.; Turner, Cristina V. (2012). "A family of nonparametric density estimation algorithms". Communications on Pure and Applied Mathematics. 66 (2): 145–164. doi:10.1002/cpa.21423. hdl:11336/8930. S2CID 17820269.
^ Papamakarios, George; Nalisnick, Eric; Jimenez Rezende, Danilo; Mohamed, Shakir; Bakshminarayanan, Balaji (2021). "Normalizing flows for probabilistic modeling and inference". Journal of Machine Learning Research. 22 (1): 2617–2680. arXiv:1912.02762.

Flow-based generative model