A **PyramidNet** is a type of convolutional network where the key idea is to concentrate on the feature map dimension by increasing it gradually instead of by increasing it sharply at each residual unit with downsampling. In addition, the network architecture works as a mixture of both plain and residual networks by using zero-padded identity-mapping shortcut connections when increasing the feature map dimension.

Per the authors, Graph Isomorphism Network (GIN) generalizes the WL test and hence achieves maximum discriminative power among GNNs.

How Powerful are Graph Neural Networks?

PyramidNet

Deep Pyramidal Residual Networks

**DenseNAS-C** is a mobile convolutional neural network discovered through the [DenseNAS](https://paperswithcode.com/method/densenas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. The basic building block is MBConvs, or inverted bottleneck residuals, from the [MobileNet](https://paperswithcode.com/method/mobilenetv2) architectures.

Source	Deep Pyramidal Residual Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com