**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.

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.

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

WideResNet

Wide Residual Networks

**ClusterFit** is a self-supervision approach for learning image representations.  Given a dataset, we (a) cluster its features extracted from a pre-trained network using k-means and (b) re-train a new network from scratch on this dataset using cluster assignments as pseudo-labels.

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