Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

@inproceedings{He2018SoftFP,
  title={Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks},
  author={Yang He and Guoliang Kang and Xuanyi Dong and Yanwei Fu and Yi Yang},
  booktitle={IJCAI},
  year={2018}
}
This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP enables the pruned filters to be updated when training the model after pruning. SFP has two advantages over previous works: (1) Larger model capacity. Updating previously pruned filters provides our approach with larger optimization space than fixing the filters to zero. Therefore, the network trained by our method has a larger… CONTINUE READING

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