Transformer-Evolution-Paper
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      • Fourier Neural Operator for Parametric Partial Differential Equations
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    • 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
  • 实验以及适用场景
  • 细节
  • 简评
  1. MHA
  2. FFT

Global Filter Networks for Image Classification

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Last updated 2 years ago

论文地址：

参考资料：

整体思路以及计算方式

对于2维输入X∈Rn×d\mathbf X\in \mathbb R^{n\times d}X∈Rn×d：

O=F−1(F(X)⊙W)∈Rn×d\mathbf O = \mathcal F^{-1}(\mathcal F(\mathbf X)\odot \mathbf W) \in \mathbb R^{n\times d}O=F−1(F(X)⊙W)∈Rn×d

其中：

W∈Rn×d\mathbf W\in \mathbb R^{n\times d}W∈Rn×d

其中F,F−1\mathcal F, \mathcal F^{-1}F,F−1分别为FFT和逆FFT，高维情形为在多个维度做FFT。

时间复杂度

O(ndlog⁡n+nd)O(nd\log n+n d)O(ndlogn+nd)。

训练以及loss

不变。

代码

实验以及适用场景

论文测试了Encoder情形，效果还可以。

细节

W\mathbf WW和序列长度有关；该方法依然不适配于Decoder情形。

简评

很自然的思路。

https://arxiv.org/abs/2107.00645
https://zhuanlan.zhihu.com/p/418500459
https://github.com/raoyongming/GFNet