Machine Learning, Deep Learning, Generative Model, Adversarial Network, Autoencoder.
likelihood of training data X. In short, we are assuming that a low-dimensional latent vector has generated our data x (x ∈ X) and we can map this latent vector to data x using a deterministic function f(z;θ) parameterized by theta which we need to evaluate (see fig. 1[1]). Under this generative