Comparer des méthodes

	Flux normalisés	Autoencodeur Variationnel
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2015	2014
Auteur d'origine	Danilo Rezende & Shakir Mohamed	Kingma, D. P. & Welling, M.
Type	Generative model via invertible transformations	Deep generative latent-variable model (encoder–decoder)
Source fondatrice	Rezende, D. J., & Mohamed, S. (2015). Variational inference with normalizing flows. International Conference on Machine Learning (ICML), 1530–1538.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Alias	Flow-Based Generative Models, Invertible Neural Networks, Exact Likelihood Models, Akışa Dayalı Üretici Modeller	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Apparentées	2	5
Résumé	Normalizing flows are a class of generative models that learn a complex probability distribution by applying a sequence of invertible, differentiable transformations to a simple base distribution such as a standard Gaussian. Introduced by Rezende and Mohamed (2015) in the context of variational inference, they enable exact likelihood computation and efficient sampling, making them a principled alternative to VAEs and GANs for density estimation and generation tasks.	The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.