Variational autoencoder models make strong assumptions concerning the distribution of "latent variables". They use a variational approach for latent representation learning, which results in an additional loss component and a specific estimator for the training algorithm called the Stochastic Gradient Variational Bayes (SGVB) estimator. It assumes that the data is generated by a directed graphical model formula_62 and that the encoder is learning an approximation formula_63 to the posterior distribution formula_64 where formula_65 and formula_66 denote the parameters of the encoder (recognition model) and decoder (generative model) respectively. The probability distribution of the latent vector of a VAE typically matches that of the training data much closer than a standard autoencoder. The objective of VAE has the following form: