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 $\mathbf{z}$.

A **Harmonic Network**, or **Harm-Net**, is a type of convolutional neural network that replaces convolutional layers with "harmonic blocks" that use [Discrete Cosine Transform](https://paperswithcode.com/method/discrete-cosine-transform) (DCT) filters. These blocks can be useful in  truncating high-frequency information (possible due to the redundancies in the spectral domain).

Harm-Net

Harmonic Convolutional Networks based on Discrete Cosine Transform

BiGAN

Adversarial Feature Learning

**Pixel2Style2Pixel**, or **pSp**, is an image-to-image translation framework that is based on a novel encoder that directly generates a series of style vectors which are fed into a pretrained [StyleGAN](https://paperswithcode.com/method/stylegan) generator, forming the extended $\mathcal{W+}$ latent space. Feature maps are first extracted using a standard feature pyramid over a [ResNet](https://paperswithcode.com/method/resnet) backbone. Then, for each of $18$ target styles, a small mapping network is trained to extract the learned styles from the corresponding feature map, where styles $(0-2)$ are generated from the small feature map, $(3-6)$ from the medium feature map, and $(7-18)$ from the largest feature map. The mapping network, map2style, is a small fully convolutional network, which gradually reduces spatial size using a set of 2-strided convolutions followed by [LeakyReLU](https://paperswithcode.com/method/leaky-relu) activations. Each generated 512 vector, is fed into [StyleGAN](https://paperswithcode.com/method/stylegan), starting from its matching affine transformation, $A$.

Source	Adversarial Feature Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com