【Pruning系列：一】Learning Efficient Convolutional Networks through Network Slimming
NS 機構 Tsinghua,Intel motivation 訓練中的剪枝 基於BN（Batch Normalization）層的廣泛使用，在BN層加入channel-wise scaling factor 並對之加L1 regularizer使之稀疏，然後裁剪scaling factor值小的部分對應權重 用 BN 層的 y 來表示卷積核的重要程度，y 小對應卷積核重要性低 γ 非常小時，送入
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相關文章
- 1. 模型壓縮：Networks Slimming-Learning Efficient Convolutional Networks through Network Slimming
- 2. 【Pruning系列：二】Learning Efficient Convolutional Networks through Network Slimming｜YOLOv3實踐 ｜Pytorch 總結
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- 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. 模型剪枝----Learning Efficient Convolutional Networks through Network Slimming
- 9. Pruning Convolutional Neural Networks For Resource Efficient Inference
- 10. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning