Pruning convolutional neural networks for resource efficent inference
這是篇2017年ICLR的會議，文章提出了一種基於泰勒展開的網絡剪枝方法。文章認爲，這裏一類的剪枝方法一般分爲三類： 一個已經訓練好的網絡 基於一定的準則在修剪和微調反覆 適時地停止 所以，這個修剪的「準則」就十分的重要，文章列出了一些以往的準則： Minimun weight：訓練時加入L1或L2正則化，並修剪權值小於某一閾值的權重 Activation：修剪經激活層後與small acti
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相關文章
- 1. Pruning Convolutional Neural Networks For Resource Efficient Inference
- 2. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
- 3. Coarse pruning of convolutional neural networks with random masks
- 4. Channel Pruning for Accelerating Very Deep Neural Networks
- 5. 《FPGM：Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration》論文筆記
- 6. 論文筆記：Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
- 7. 《Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks》論文筆記
- 8. Understanding Convolutional Neural Networks for NLP
- 9. Convolutional Neural Networks for Sentence Classification
- 10. Learning Convolutional Neural Networks for Graphs