We present ResMLP, an architecture built entirely upon multi-layer
perceptrons for image classification. It is a simple residual network that
alternates (i) a linear layer in which image patches interact, independently
and identically across channels, and (ii) a two-layer feed-forward network in
which channels interact independently per patch. When trained with a modern
training strategy using heavy data-augmentation and optionally distillation, it
attains surprisingly good accuracy/complexity trade-offs on ImageNet. We also
train ResMLP models in a self-supervised setup, to further remove priors from
employing a labelled dataset. Finally, by adapting our model to machine
translation we achieve surprisingly good results.
We share pre-trained models and our code based on the Timm library.

ResMLP 是一种基于多层感知器的图像分类架构，在现代训练策略（包括数据扩增和引入先验）的支持下在 ImageNet 上取得了令人惊讶的良好准确性 / 复杂度平衡，并通过自监督学习进一步去除了基于带标签数据集的先验知识。同时，将该模型应用于机器翻译领域也能获得令人惊讶的效果。