The variational autoencoder is a model introduced in Kingma and that combines an "encoder" and a "decoder" to form a variational inference model. In specific, given a dataset with a marginal likelihood p θ (x), the objective of the VAE is to learn the parameter set {θ } that most closely reproduces the data's distribution p(x) Kingma and Welling . VAEs operate under an evidence lower bound (ELBO) maximization objective that leads to a joint maximisation of the marginal likelihood p θ (x) and a minimization of the Kullback-Leibler (KL) divergence of the decoder-approximated posterior q φ (z|x) from the true posterior p θ (z|x), where z represents the embedding variables of the latent space and p θ (z) is the latent space prior, which for the standard VAE is a Gaussian distribution.