Generative Adversarial Networks in Detail

Another way to explain GANs is through the probabilistic formulation we used on variational autoencoders.

GANs follow a different approach in finding the probability distribution of the data $p_{data}(x)$.

Instead of computing or the approximate $p_{data}(x)$, we only care about the ability to sample data from the distribution.

But what does that actually mean?

If we assume that our data $x_i$ follow a probability distribution $p_{data}(x)$, we will want to build a model that allows us to draw samples from $p_{data}(x)$.

As we did with VAE, we again introduce a latent variable $z$ with a prior distribution $p(z)$. $p(z)$ is usually a simple ...