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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
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  2. RightProduct

XCiT: Cross-Covariance Image Transformers

PreviousUFO-ViT: High Performance Linear Vision Transformer without SoftmaxNextSimpleTRON: Simple Transformer with O(N) Complexity

Last updated 2 years ago

论文地址：

整体思路以及计算方式

输入：

  • X∈Rn×d1X\in \mathbb R^{n\times d_1}X∈Rn×d1​

  • Q,K,V=XWQ,XWK,XWV∈Rn×d2Q,K,V=XW_Q, XW_K, XW_V\in \mathbb R^{n\times d_2}Q,K,V=XWQ​,XWK​,XWV​∈Rn×d2​

  • Q=Norm(Q),K=Norm(K)Q= \mathrm{Norm}(Q), K=\mathrm{Norm}(K)Q=Norm(Q),K=Norm(K)

  • O=VSoftmax(K⊤Q)Wo∈Rn×d1O=V\mathrm{Softmax}(K^{\top} Q) W_o\in \mathbb R^{n\times d_1}O=VSoftmax(K⊤Q)Wo​∈Rn×d1​（分组计算）

时间复杂度

假设有hhh个分组，那么时间复杂度为O(n(d/h)2×h)=O(nd2/h)O(n(d/h)^2\times h)=O(nd^2/h)O(n(d/h)2×h)=O(nd2/h)。

训练以及loss

不变。

代码

实验以及适用场景

适用于Encoder，作者进行了视觉任务，效果都不错。

细节

作者在Attention和FFN之间增加了一个模块，带来了不少提升，但是不加这个模块性能一般；另一方面，计算内积的同时增加了分组操作，这部分需要看源码。

简评

这篇思路过于简单，不知道该模块单独使用是否起作用。

https://arxiv.org/abs/2106.09681
https://github.com/facebookresearch/xcit