Machine Learning, Deep Learning, Generative Model, Adversarial Network, Autoencoder.
probabilistic graphical model rooted in Bayesian inference i.e., the model aims to learn the underlying probability distribution of the training data so that it could easily sample new data from that learned distribution. The idea is to learn a low-dimensional latent representation of the training