Transformer-Evolution-Paper
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      • Synthesizer: Rethinking Self-Attention in Transformer Models
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      • Swin Transformer V2: Scaling Up Capacity and Resolution
      • Skip-Attention: Improving Vision Transformers by Paying Less Attention
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    • Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention
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    • When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute
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    • Parameter-Efficient Fine-Tuning without Introducing New Latency
    • Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
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    • LLM Details Summary
    • What Language Model to Train if You Have One Million GPU Hours?
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  • 整体思路以及计算方式
  • 时间复杂度
  • 训练以及loss
  • 代码
  • 细节
  • 简评
  1. MHA
  2. Others

Swin Transformer V2: Scaling Up Capacity and Resolution

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Last updated 2 years ago

论文地址：

整体思路以及计算方式

对Swin-Transformer做了一些工程上的改进：

  1. PreNorm换成PostNorm；

  2. q,k\mathbf q, \mathbf kq,k做内积之前先过了归一化：q=q/∥q∥2,k=k/∥k∥2\mathbf q= \mathbf q/\|\mathbf q \|_2, \mathbf k= \mathbf k/\|\mathbf k \|_2q=q/∥q∥2​,k=k/∥k∥2​；

  3. 增加相对位置编码：f(log⁡(i−j))f(\log (i-j))f(log(i−j))；

图示：

时间复杂度

不考虑。

训练以及loss

不考虑。

代码

细节

细节挺多的，复现的时候细读。

简评

做了一些工程上的改进，代码库中也有cuda相关代码，值得复现。

https://github.com/microsoft/Swin-Transformer
https://arxiv.org/abs/2111.09883