EESP
Extremely Efficient Spatial Pyramid of Depth-wise Dilated Separable Convolutions
An **EESP Unit**, or  Extremely Efficient Spatial Pyramid of Depth-wise Dilated Separable Convolutions, is an image model block designed for edge devices. It was proposed as part of the [ESPNetv2](https://paperswithcode.com/method/espnetv2) CNN architecture. 

This building block is based on a reduce-split-transform-merge strategy. The EESP unit first projects the high-dimensional input feature maps into low-dimensional space using groupwise pointwise convolutions and then learns the representations in parallel using depthwise dilated separable convolutions with different dilation rates. Different dilation rates in each branch allow the EESP unit to learn the representations from a large effective receptive field. To remove the gridding artifacts caused by dilated convolutions, the EESP fuses the feature maps using [hierarchical feature fusion](https://paperswithcode.com/method/hierarchical-feature-fusion) (HFF).
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
https://paperswithcode.com/paper/espnetv2-a-light-weight-power-efficient-and
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network