An **Efficient Spatial Pyramid (ESP)** is an image model block based on a factorization principle that decomposes a standard [convolution](https://paperswithcode.com/method/convolution) into two steps: (1) point-wise convolutions and (2) spatial pyramid of dilated convolutions. The point-wise convolutions help in reducing the computation, while the spatial pyramid of dilated convolutions re-samples the feature maps to learn the representations from large effective receptive field. This allows for increased efficiency compared to another image blocks like [ResNeXt](https://paperswithcode.com/method/resnext) blocks and Inception modules.

**Effective Squeeze-and-Excitation Block** is an image model block based on squeeze-and-excitation, the difference being that one less FC layer is used. The authors note the SE module has a limitation: channel information loss due to dimension reduction. For avoiding high model complexity burden, two FC layers of the SE module need to reduce channel dimension. Specifically, while the first FC layer reduces input feature channels $C$ to $C/r$ using reduction ratio $r$, the second FC layer expands the reduced channels to original channel size $C$. As a result, this channel dimension reduction causes channel information loss. Therefore, effective SE (eSE) uses only one FC layer with $C$ channels instead of two FCs without channel dimension reduction, which maintains channel information.

Effective Squeeze-and-Excitation Block

CenterMask : Real-Time Anchor-Free Instance Segmentation

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

**Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of wide residual blocks.

Source	ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com