《Learning both Weights and Connections for Efficient Neural Networks》論文筆記
1. 論文思想 深度神經網絡在計算與存儲上都是密集的，這就妨礙了其在嵌入式設備上的運用。爲了解決該問題，便需要對模型進行剪枝。在本文中按照網絡量級的排序，使得通過只學習重要的網絡連接在不影響精度的情況下減少存儲與計算量。論文中的方法分爲三步：首先，使用常規方法訓練模型；使用剪枝策略進行模型修剪；在修剪模型的基礎上進行finetune。經過試驗證明改文章提出的方法使得AlexNet的大小減小了9倍，
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相關文章
- 1. 【Learning both Weights and Connections for Efficient Neural Networks】論文筆記
- 2. 論文品讀：Learning both Weights and Connections for Efficient Neural Networks
- 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. 論文筆記系列-Simple And Efficient Architecture Search For Neural Networks
- 8. Learning Convolutional Neural Networks for Graphs論文筆記
- 9. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】論文筆記
- 10. 【論文閱讀筆記】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference