代码开源：Channel Pruning for Accelerating Very Deep Neural Networks
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代码开源：Channel Pruning for Accelerating Very Deep Neural Networks
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这是我们今年 ICCV 2017 的工作。
基于CNN每一层都是稀疏地假设，我们考虑是否能找到neuron之间的关系，仅留下最具有代表性地neuron？
分为两步解决问题：
- 找出每一层具有代表性地neuron，我们利用lasso regression来进行类似model selection的过程。将剩余的neuron去掉（pruning）。
- 利用剩下的代表性neuron来重构（reconstruction）这一层原本的输出。
实验效果：
- VGG-16, 4倍加速, Top-5 accuracy 下降 0.0
- Xception-50, 2倍加速, Top-5 accuracy 下降 1.0
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