自注意機制論文學習: On the Relationship between Self-Attention and Convolutional Layers
背景 transformer的提出對NLP領域的研究有很大的促進作用，得益於attention機制，特別是self-attention，就有研究學者將attention/self-attention機制引入計算機視覺領域中，也取得了不錯的效果[1][2]。該論文[4]側重於從理論和實驗去驗證self-attention[3]可以代替卷積網絡獨立進行類似卷積的操作，給self-attention在圖
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相關文章
- 1. Relationship between APT and dpkg
- 2. The relationship between population distribution and venues division in Manhattan
- 3. 《論文》：注意力機制
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- 6. Relationship between the Hessian and Covariance Matrix for Gaussian Random Varia
- 7. 深度學習論文筆記（注意力機制）——CBAM: Convolutional Block Attention Module
- 8. [論文筆記]Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
- 9. 論文SDP + RCNN | Exploit All the Layers: Fast and Accurate CNN Object Detector with SDP and CRC
- 10. 【論文學習】Densely Connected Convolutional Networks 學習