【Fast Algorithms for Convolutional Neural Networks】論文筆記
這篇文章基於Winograd開創的最小濾波算法，介紹了一類新的卷積神經網絡快速算法。 針對卷積計算方式而言，與直接卷積相比，該算法可以將卷積層的算術複雜度降低4倍。 算法部分 對於最小濾波算法而言，使用r維濾波器計算m個輸出(稱之爲F(m,r))需要m+r-1次乘法計算。 標準計算而言的話，需要m×r次乘法計算。 對於1-D卷積： 輸入x=[1,2,3,4]，卷積核大小爲3×1(r=3)，k=[1
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相關文章
- 1. 論文閱讀-Fast Algorithms for Convolutional Neural Networks
- 2. Learning Convolutional Neural Networks for Graphs論文筆記
- 3. [論文筆記] [2016] [NIPS] Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- 4. 論文筆記：Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- 5. Diffusion-Convolutional Neural Networks論文筆記
- 6. 論文筆記——Factorized Convolutional Neural Networks
- 7. 論文筆記：Recurrent Convolutional Neural Networks for Text Classification
- 8. 論文《Convolutional Neural Networks for Sentence Classification》總結
- 9. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 10. 《Convolutional Neural Networks for Sentence Classification》論文研讀