Transformer-Evolution-Paper
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    • When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism
    • Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?
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    • FFT
      • Fourier Neural Operator for Parametric Partial Differential Equations
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      • Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
      • FNet: Mixing Tokens with Fourier Transforms
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      • On Learning the Transformer Kernel
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      • Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
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      • Blockwise Self-Attention for Long Document Understanding
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      • Simple Local Attentions Remain Competitive for Long-Context Tasks
      • You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
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      • Synthesizer: Rethinking Self-Attention in Transformer Models
      • Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kern
      • Combiner Full Attention Transformer with Sparse Computation Cost
      • Ripple Attention for Visual Perception with Sub-quadratic Complexity
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    • ReZero is All You Need Fast Convergence at Large Depth
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    • RealFormer Transformer Likes Residual Attention
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    • A Simple and Effective Positional Encoding for Transformers
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    • Transformer with a Mixture of Gaussian Keys
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    • Accelerating Neural Transformer via an Average Attention Network
    • Do Transformer Modifications Transfer Across Implementations and Applications?
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    • Do Transformer Modifications Transfer Across Implementations and Applications?
    • Why self-attention is Natural for Sequence-to-Sequence Problems? A Perspective from Symmetries
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    • Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
    • Parallelizing Legendre Memory Unit Training
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    • Pretraining Without Attention
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    • Simple Hardware-Efficient Long Convolutions for Sequence Modeling
    • Time-aware large kernel convolutions
    • Resurrecting Recurrent Neural Networks for Long Sequences
    • CKConv: Continuous Kernel Convolution For Sequential Data
    • FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
    • Towards a General Purpose CNN for Long Range Dependencies in ND
  • Rnn
    • When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute
    • Linear Transformers Are Secretly Fast Weight Programmers
    • Going Beyond Linear Transformers with Recurrent Fast Weight Programmers
    • Parallelizing Linear Recurrent Neural Nets Over Sequence Length
    • Quasi-recurrent neural networks
  • CrossAttention
    • Neural Machine Translation in Linear Time
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    • Extrapolation
      • Parallel Context Windows for Large Language Models
      • Structured Prompting: Scaling In-Context Learning to 1,000 Examples
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  • Peft
    • Parameter-Efficient Fine-Tuning without Introducing New Latency
    • Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
  • LLM
    • 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
  • 实验以及适用场景
  • 细节
  • 简评
  1. FFN

Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?

PreviousWhen Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention MechanismNextHead

Last updated 2 years ago

论文地址：

整体思路以及计算方式

利用Sparse MLP进行Token Mixing：

实现起来也非常简单，见论文伪代码。

时间复杂度

O(nd2)O(nd^2)O(nd2)。

训练以及loss

代码

实验以及适用场景

只适用于CV场景。

细节

暂无。

简评

很简单的思路，也比较优雅。

https://github.com/microsoft/SPACH
https://arxiv.org/abs/2109.05422