Variational Autoencoder with Implicit Optimal Priors

Understand

The variational autoencoder (VAE) is a powerful generative model that can estimate the probability of a data point by using latent variables.

• In the VAE, the posterior of the latent variable given the data point is regularized by the prior of the latent variable using Kullback Leibler (KL) divergence.
• Although the standard Gaussian distribution is usually used for the prior, this simple prior incurs over-regularization.
• As a sophisticated prior, the aggregated posterior has been introduced, which is the expectation of the posterior over the data distribution.