Generative Adversarial Networks

Once we discussed latent variable models, we claimed that they naturally define a generative process by first sampling latents z ∼ p(z) and then generating observables x ∼ pθ(x|z). That is nice! However, the problem appears when we start thinking about training. To be more precise, the training objective is an issue. Why? Well, the probability theory tells us to get rid of all unobserved random variables by marginalizing them out.