Adversarial autoencoder (AAE) is a generative model produced by the union of VAE and GAN. To explain the model, we start by defining the following terms:
- x: Autoencoder input
- z: Code produced from x,
- p(z): The distribution we want to impose
- q(z|x): Distribution learned from the encoder
- p(x|z): Distribution learned from the decoder
- pdata: Distribution of the data
- p(x): Distribution of the model
We consider the function q(z|x) as a posterior distribution of q(z), which is defined as follows:
We try to impose the equality q(z)=p(z) on the model. The difference with a VAE is due to the fact that what drives q (z) towards ...
