【To prune, or not to prune: exploring the efficacy of pruning for model compression】論文筆記
[ICLR’18]論文鏈接：https://arxiv.org/abs/1710.01878 Tensorflow修剪庫參考：https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/model_pruning 這篇文章的重點在於比較一個大疏模型和一個小密模型之間的模型精度和尺寸權衡上，試圖仔細研究模型修剪作爲模
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相關文章
- 1. 《To prune, or not to prune：exploring the efficacy of pruning for model compression》論文筆記
- 2. 【論文閱讀筆記】Rethinking the value of Network Pruning
- 3. git prune, git remote prune, git fetch --prune 三者異同
- 4. 【ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression】論文筆記
- 5. Sweep and Prune Algorithm - Introduction
- 6. 《RETHINKING THE VALUE OF NETWORK PRUNING》論文筆記
- 7. Reading Note: ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
- 8. 論文筆記——PRUNING FILTERS FOR EFFICIENT CONVNETS
- 9. 論文筆記《DermaKNet: Incorporating the knowledge of dermatologists to Convolutional Neural Networks for 》
- 10. To forgive or not