Class-MLP
Class-MLP
**Class-MLP** is an alternative to [average pooling](https://paperswithcode.com/method/average-pooling), which is an adaptation of the class-attention token introduced in [CaiT](https://paperswithcode.com/method/cait). In CaiT, this consists of two layers that have the same structure as the [transformer](https://paperswithcode.com/method/transformer), but in which only the class token is updated based on the frozen patch embeddings. In Class-MLP, the same approach is used, but after aggregating the patches with a [linear layer](https://paperswithcode.com/method/linear-layer), we replace the [attention-based interaction](https://paperswithcode.com/method/scaled) between the class and patch embeddings by simple linear layers, still keeping the patch embeddings frozen. This increases the performance, at the expense of adding some parameters and computational cost. This pooling variant is referred to as “class-MLP”, since the purpose of these few layers is to replace average pooling.
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