模型壓縮：Networks Slimming-Learning Efficient Convolutional Networks through Network Slimming
Network Slimming-Learning Efficient Convolutional Networks through Network Slimming（Paper） 2017年ICCV的一篇paper，思路清晰，骨骼清奇~~ 創新點： 1. 利用batch normalization中的縮放因子γ 作爲重要性因子，即γ越小，所對應的channel不太重要，就可以裁剪（pruning
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- 2. 模型壓縮之Learning Efficient Convolutional Networks through Network Slimming
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- 4. 《Learning Efficient Convolutional Networks through Network Slimming》論文筆記
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- 9. 【Pruning系列：二】Learning Efficient Convolutional Networks through Network Slimming｜YOLOv3實踐 ｜Pytorch 總結
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相關文章
- 1. 模型壓縮：Networks Slimming-Learning Efficient Convolutional Networks through Network Slimming
- 2. 模型壓縮之Learning Efficient Convolutional Networks through Network Slimming
- 3. 模型剪枝----Learning Efficient Convolutional Networks through Network Slimming
- 4. 《Learning Efficient Convolutional Networks through Network Slimming》論文筆記
- 5. 【論文閱讀筆記】Learning Efficient Convolutional Networks through Network Slimming
- 6. DL經典文章翻譯2：Learning Efficient Convolutional Networks through Network Slimming
- 7. [論文理解] Learning Efficient Convolutional Networks through Network Slimming
- 8. 【Pruning系列：一】Learning Efficient Convolutional Networks through Network Slimming
- 9. 【Pruning系列：二】Learning Efficient Convolutional Networks through Network Slimming｜YOLOv3實踐 ｜Pytorch 總結
- 10. 模型壓縮