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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. Others

Object-Centric Learning with Slot Attention

PreviousDo Transformer Modifications Transfer Across Implementations and Applications?NextDo Transformer Modifications Transfer Across Implementations and Applications?

Last updated 2 years ago

论文地址：

参考资料：

整体思路以及计算方式

对任务背景没有特别的了解，感觉是一种抽特征的方式，直接讨论计算方式，忽略Normlize相关部分：

  • X∈RN×d1\mathbf X\in \mathbb R^{N\times d_1}X∈RN×d1​

  • S∼N(μ,diag⁡(σ))∈RK×d2\mathbf {S} \sim \mathcal{N}(\mu, \operatorname{diag}(\sigma)) \in \mathbb{R}^{K \times d_2}S∼N(μ,diag(σ))∈RK×d2​（代表Slots\mathbf {Slots}Slots）

  • for t=0,…,T−1t=0,\ldots ,T-1t=0,…,T−1:

    • Sprev=S∈RK×d2\mathbf {S}_{\mathrm{prev}}=\mathbf{S}\in \mathbb R^{K\times d_2}Sprev​=S∈RK×d2​

    • Q=SWq∈RK×d,K=XWk∈RN×d,V=XWv∈RN×d\mathbf Q= \mathbf{S}\mathbf W_q \in \mathbb R^{K\times d},\mathbf K=\mathbf X\mathbf W_k\in \mathbb R^{N\times d},\mathbf V=\mathbf X\mathbf W_v \in \mathbb R^{N\times d}Q=SWq​∈RK×d,K=XWk​∈RN×d,V=XWv​∈RN×d

    • A=Softmax(QK⊤,dim=0)∈RK×N\mathrm{A}=\mathrm{Softmax}(\mathbf Q\mathbf K^{\top} , \mathrm{dim}=0)\in \mathbb R^{K\times N}A=Softmax(QK⊤,dim=0)∈RK×N

    • U=AV∈RK×d\mathbf{U}=\mathbf A\mathbf V\in \mathbb R^{K\times d}U=AV∈RK×d

    • S=GRU(Sprev,U)∈RK×d2\mathbf{S}= \mathrm{GRU}(\mathbf {S}_{\mathrm{prev}}, \mathbf U) \in \mathbb R^{K\times d_2}S=GRU(Sprev​,U)∈RK×d2​

时间复杂度

MHA\mathrm{MHA}MHA的时间复杂度为O(KNd)O(KNd)O(KNd)，总时间复杂度为O(TKNd)O(TKNd)O(TKNd)。

训练以及loss

没有变化。

代码

实验以及适用场景

作者进行的实验比较简单，这里不进行讨论。

细节

略过。

简评

个人理解是一种抽特征的方式，不知道能否适用于NLP任务。

https://arxiv.org/pdf/2006.15055.pdf
https://zhuanlan.zhihu.com/p/344979830
https://github.com/lucidrains/slot-attention
https://github.com/google-research/google-research/tree/master/slot_attention