一、motivation

Vision transformer (ViT) models exhibit substandard optimizability. In particular,

they are sensitive to the choice of optimizer (AdamW vs. SGD), optimizer hyperpa

rameters, and training schedule length. In comparison, modern convolutional neural

networks are easier to optimize。

 问题假设为：

In this work, we conjecture

that the issue lies with the patchify stem of ViT models, which is implemented by

a stride-p p×p convolution (p = 16 by default) applied to the input image. This

large-kernel plus large-stride convolution runs counter to typical design choices

of convolutional layers in neural networks.

In this paper we hypothesize that the issues lies primarily in the early visual processing performed by ViT.

二、solution

个人认为文章里面有一个很好的思路：