ResMLP
Residual Multi-Layer Perceptrons
**Residual Multi-Layer Perceptrons**, or **ResMLP**, is an architecture built entirely upon [multi-layer perceptrons](https://paperswithcode.com/methods/category/feedforward-networks) for image classification. It is a simple [residual network](https://paperswithcode.com/method/residual-connection) that alternates (i) a [linear layer](https://paperswithcode.com/method/linear-layer) in which image patches interact, independently and identically across channels, and (ii) a two-layer [feed-forward network](https://paperswithcode.com/method/feedforward-network) in which channels interact independently per patch. At the end of the network, the patch representations are average pooled, and fed to a linear classifier.

[Layer normalization](https://paperswithcode.com/method/layer-normalization) is replaced with a simpler [affine transformation](https://paperswithcode.com/method/affine-operator), thanks to the absence of self-attention layers which makes training more stable. The affine operator is applied at the beginning ("pre-normalization") and end ("post-normalization") of each residual block. As a pre-normalization, Aff replaces LayerNorm without using channel-wise statistics. Initialization is achieved as $\mathbf{\alpha}=\mathbf{1}$, and $\mathbf{\beta}=\mathbf{0}$. As a post-normalization, Aff is similar to [LayerScale](https://paperswithcode.com/method/layerscale) and $\mathbf{\alpha}$ is initialized with the same small value.
ResMLP: Feedforward networks for image classification with data-efficient training
https://paperswithcode.com/paper/resmlp-feedforward-networks-for-image
ResMLP: Feedforward networks for image classification with data-efficient training