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Variational autoencoder - Wikipedia

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In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling.[1] It is part of the families of probabilistic graphical models and variational Bayesian methods.[2] In addition to being seen as an autoencoder neural network architecture, variational autoencoders can also be studied within the mathematical formulation of variational Bayesian methods, connecting a neural encoder network to its decoder through a probabilistic latent space (for example, as a multivariate Gaussian distribution) that corresponds to the parameters of a variational distribution. Thus, the encoder maps each point (such as an image) from a large complex dataset into a distribution within the latent space, rather than to a single point in that space. The decoder has the opposite function, which is to map from the latent space to the input space, again according to a distribution (although in practice, noise is rarely added during

Variational autoencoder - Wikipedia Jump to content From Wikipedia, the free encyclopedia Deep learning generative model to encode data representation The basic scheme of a variational autoencoder. The model receives x {\displaystyle x} as input. The encoder compresses it into the latent space. The decoder receives as input the information sampled from the latent space and produces x ′ {\displaystyle {x'}} as similar as possible to x {\displaystyle x} . Part of a series on Machine learning and data mining Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervi

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