Variational Autoencoder (VAE) and Variational Bayesian methods Going through the code is almost the best way to explain the Variational Autoencoder.

A Variational Autoencoder (VAE) is a generative model used to generate new data similar to the training dataset. It is an extension of the autoencoder that introduces variational inference to learn ...

The variational autoencoder (VAE) has been used in a myriad of applications, e.g., dimensionality reduction and generative modeling. VAE uses a specific model for stochastic sampling in latent space.

An implicit-derivative-based reparameterization trick enables the use of a gamma distribution in a variational autoencoder. The latent variables in the generative model are inferred using the ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...