Layer Pruning for Accelerating Very Deep Neural Networks
arXiv:1910.12727v1
Originality Incremental advance
AI Analysis
This work addresses the computational efficiency challenge for deep learning practitioners, though it appears incremental as it builds on existing pruning techniques.
The paper tackles the problem of accelerating very deep neural networks by proposing an adaptive pruning method that reduces parameters by half while maintaining or improving accuracy compared to the baseline.
In this paper, we propose an adaptive pruning method. This method can cut off the channel and layer adaptively. The proportion of the layer and the channel to be cut is learned adaptively. The pruning method proposed in this paper can reduce half of the parameters, and the accuracy will not decrease or even be higher than baseline.