近期关于感知器MLP的最新研究

• 1. MLP-Mixer: An all-MLP Architecture for Vision
• 2. ResMLP: Feedforward networks for image classification with data-efficient training
• 3. Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet
• 4. RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

1. MLP-Mixer: An all-MLP Architecture for Vision

Google Research, Brain Team团队的工作
文章链接： https://arxiv.org/pdf/2105.01601.pdf
代码链接：https://github.com/google-research/vision_transformer/tree/linen

2. ResMLP: Feedforward networks for image classification with data-efficient training

Facebook AI团队的工作
文章链接：https://arxiv.org/abs/2105.03404
代码链接：https://github.com/lucidrains/res-mlp-pytorch

3. Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet

牛津大学团队的工作
文章链接：https://arxiv.org/abs/2105.02723
代码链接：https://github.com/lukemelas/do-you-even-need-attention

4. RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

清华大学&旷视科技团队联合工作
文章链接：https://arxiv.org/abs/2105.01883
代码链接：https://github.com/DingXiaoH/RepMLP