BiGAN
Bidirectional GAN
A **BiGAN**, or **Bidirectional GAN**, is a type of generative adversarial network where the generator  not only maps latent samples to generated data, but also has an inverse mapping from data to the latent representation. The motivation is to make a type of GAN that can learn rich representations for us in applications like unsupervised learning.

In addition to the generator $G$ from the standard [GAN](https://paperswithcode.com/method/gan) framework, BiGAN includes an encoder $E$ which maps data $\mathbf{x}$ to latent representations $\mathbf{z}$. The BiGAN discriminator $D$ discriminates not only in data space ($\mathbf{x}$ versus $G\left(\mathbf{z}\right)$), but jointly in data and latent space (tuples $\left(\mathbf{x}, E\left(\mathbf{x}\right)\right)$ versus $\left(G\left(z\right), z\right)$), where the latent component is either an encoder output $E\left(\mathbf{x}\right)$ or a generator input $\mathbf{z}$.
Adversarial Feature Learning
https://paperswithcode.com/paper/adversarial-feature-learning
Adversarial Feature Learning