title: ResMLP: Feedforward networks for image classification with data-efficient training
abstract: 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 will share our code based on the Timm library and  pre-trained models.
authors: Hugo Touvron Piotr Bojanowski Mathilde Caron Matthieu Cord Alaaeldin El-Nouby Edouard Grave Gautier Izacard Armand Joulin Gabriel Synnaeve Jakob Verbeek Herve Jegou
hIndex: Armand Joulin 49