r/deeplearning - Can someone briefly explain the latent loss part of Variational autoencoder?

They consist of a probabilistic decoder and a probabilistic encoder. Probabilistic encoder encodes the input data into a Gaussian multivariate distribution, such that it produces a mean vector and a diagonal covariance matrix. The dimension of this distribution is determined by how many nodes you have in the latent layer. Then, probabilistic decoder samples the encoded distribution and creates the reconstructed data after forward propagating the sample. The loss function (to be minimized) consists of two parts: negative reconstruction likelihood ensuring that it will be likely to produce data similar to those in training dataset, and KL divergence from a prior Gaussian (usually N(0,I)) which acts as a regularizer.