Also known as: VAE
VAE (variational autoencoder) is a generative model that learns a probabilistic latent representation of input data. It uses variational inference to generate modified, reconstructed, or novel examples similar to the training data.
A type of autoencoder that leverages the discrepancy between inputs and outputs to generate modified versions of the inputs. Variational autoencoders are useful for generative AI. VAEs are based on variational inference: a technique for estimating the parameters of a probability model.