**DCGAN**, or **Deep Convolutional GAN**, is a generative adversarial network architecture. It uses a couple of guidelines, in particular:

- Replacing any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator).
- Using batchnorm in both the generator and the discriminator.
- Removing fully connected hidden layers for deeper architectures.
- Using [ReLU](https://paperswithcode.com/method/relu) activation in generator for all layers except for the output, which uses tanh.
- Using LeakyReLU activation in the discriminator for all layer.

PolyConv learns continuous distributions as the convolutional filters to share the weights across different vertices of graphs or points of point clouds.

PolyConv

PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation

DCGAN

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

**BiSeNet V2** is a two-pathway architecture for real-time semantic segmentation. One pathway is designed to capture the spatial details with wide channels and shallow layers, called Detail Branch. In contrast, the other pathway is introduced to extract the categorical semantics with narrow channels and deep layers, called Semantic Branch. The Semantic Branch simply requires a large receptive field to capture semantic context, while the detail information can be supplied by the Detail Branch. Therefore, the Semantic Branch can be made very lightweight with fewer channels and a fast-downsampling strategy. Both types of feature representation are merged to construct a stronger and more comprehensive feature representation.

Source	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com