論文《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
因爲對深度壓縮中的剪枝不太理解遂讀了原文作者更早的這篇詳細講網絡剪枝的文章點擊打開鏈接 剪枝的過程爲： 1.首先剪枝的前提是對已完成訓練的網絡 2.進行剪枝 要點：根據一個閾值去裁剪參數 a.閾值的確定：首先這個閾值相關於這一層權重的標準差（The pruning threshold is chosen as a quality parameter multiplied by the stan
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相關文章
- 1. 【Learning both Weights and Connections for Efficient Neural Networks】論文筆記
- 2. 《Learning both Weights and Connections for Efficient Neural Networks》論文筆記
- 3. 論文品讀：Learning both Weights and Connections for Efficient Neural Networks
- 4. 【論文閱讀】韓鬆《Efficient Methods And Hardware For Deep Learning》節選《Learning both Weights and Connections 》
- 5. 網絡模型剪枝-論文閱讀《Learning both Weights and Connections for Efficient Neural Networks》
- 6. 深度網絡推理加速（Learning both Weights and Connections for Efficient Neural Networks）
- 7. 【論文閱讀筆記】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- 8. 論文閱讀筆記：ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- 9. Machine Learning & Deep Learning 論文閱讀筆記
- 10. 論文閱讀筆記《Edge-Labeling Graph Neural Network for Few-shot Learning》