Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ResMLP: Feedforward networks for image classification with
  data-efficient training   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.

16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys