論文品讀：Learning both Weights and Connections for Efficient Neural Networks
https://arxiv.org/abs/1506.02626v3 這是一篇關於模型壓縮的15年文章，到目前爲止（18年11月）有450的被引 文章介紹了一種參數剪枝（weights pruning）方法，應該算是最基礎的一種方法了，直接按照參數是否大於某個閾值來判斷哪些參數是重要的，哪些參數是不重要。 在不降低精度的前提下，在VGG-16上取得了13倍的參數壓縮率，從138M個參數到10.8M
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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《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
- 4. 網絡模型剪枝-論文閱讀《Learning both Weights and Connections for Efficient Neural Networks》
- 5. 【論文閱讀】韓鬆《Efficient Methods And Hardware For Deep Learning》節選《Learning both Weights and Connections 》
- 6. 深度網絡推理加速（Learning both Weights and Connections for Efficient Neural Networks）
- 7. 論文閱讀——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 8. Neural Networks and Deep Learning-引論
- 9. 論文筆記系列-Simple And Efficient Architecture Search For Neural Networks
- 10. Neural Networks and Deep Learning -- Class 3: Shallow neural networks