【論文閱讀筆記】Learning Efficient Convolutional Networks through Network Slimming
論文地址：Network Slimming 論文總結 本文提出了一種channel-level的裁剪方案，可以通過稀疏化尺度因子（BN層的scaling factor）來裁掉「不重要」的channel。 文中的方案爲： 在訓練時，對BN層的scaling factor施加 L 1 L_1 L1正則化，在訓練網絡的同時得到稀疏化的尺度因子； 裁掉低於指定閾值的channel；【（1）設定裁
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相關文章
- 1. 《Learning Efficient Convolutional Networks through Network Slimming》論文筆記
- 2. 模型壓縮：Networks Slimming-Learning Efficient Convolutional Networks through Network Slimming
- 3. [論文理解] Learning Efficient Convolutional Networks through Network Slimming
- 4. DL經典文章翻譯2：Learning Efficient Convolutional Networks through Network Slimming
- 5. 模型壓縮之Learning Efficient Convolutional Networks through Network Slimming
- 6. 【Pruning系列：一】Learning Efficient Convolutional Networks through Network Slimming
- 7. 模型剪枝----Learning Efficient Convolutional Networks through Network Slimming
- 8. Machine Learning & Deep Learning 論文閱讀筆記
- 9. 【Pruning系列：二】Learning Efficient Convolutional Networks through Network Slimming｜YOLOv3實踐 ｜Pytorch 總結
- 10. 論文閱讀筆記（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）