自注意机制论文学习: On the Relationship between Self-Attention and Convolutional Layers

时间 2020-12-26

标签论文学习繁體版

原文原文链接

背景 transformer的提出对NLP领域的研究有很大的促进作用，得益于attention机制，特别是self-attention，就有研究学者将attention/self-attention机制引入计算机视觉领域中，也取得了不错的效果[1][2]。该论文[4]侧重于从理论和实验去验证self-attention[3]可以代替卷积网络独立进行类似卷积的操作，给self-attention在图

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1. Relationship between APT and dpkg
2. The relationship between population distribution and venues division in Manhattan
3. 《论文》：注意力机制
4. The trust relationship between this workstation and the primary domain failed
5. What's the difference between →, ⊢ and ⊨ ?
6. Relationship between the Hessian and Covariance Matrix for Gaussian Random Varia
7. 深度学习论文笔记（注意力机制）——CBAM: Convolutional Block Attention Module
8. [论文笔记]Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
9. 论文SDP + RCNN | Exploit All the Layers: Fast and Accurate CNN Object Detector with SDP and CRC
10. 【论文学习】Densely Connected Convolutional Networks 学习

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