Can attention enable MLPs to catch up with CNNs?

被引：0

作者：

Meng-Hao Guo ^{[1
]}

Zheng-Ning Liu ^{[1
]}

Tai-Jiang Mu ^{[1
]}

Dun Liang ^{[1
]}

Ralph R.Martin ^{[2
]}

Shi-Min Hu ^{[1
]}

机构：

[1] BNRist,Department of Computer Science and Technology,Tsinghua University

[2] Cardiff University

来源：

Computational Visual Media | 2021年 / 7卷 / 03期

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the first week of May 2021,researchers from four different institutions:Google,Tsinghua University,Oxford University,and Facebook shared their latest work[1–4]on ar Xiv.org at almost the same time,each proposing new learning architectures,consisting mainly of linear layers,claiming them to be comparable or superior to convolutional-based models.

引用

页码：283 / 288

页数：6

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