Flow-based generative model: Difference between revisions

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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''',<ref>{{cite arXiv | eprint=1505.05770| author1=Danilo Jimenez Rezende| last2=Mohamed| first2=Shakir| title=Variational Inference with Normalizing Flows| year=2015| class=stat.ML}}</ref> which is a statistical method using the [[Probability density function#Function of random variables and change of variables in the probability density function|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 minimizedoptimized 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 [[Autoencoder#Variational autoencoder (VAE)|variational autoencoder (VAE)]] and [[generative adversarial network]] do not explicitly represent the likelihood function.