Abstract:Deploying deep learning models in embedded terminals is essential for applications with real-time reasoning requirements. In order to make the model run efficiently in the embedded end with limited resources, we propose a model compression method combining multi-factor channel pruning and knowledge distillation. In the process of network sparsity, this method uses the double factors of the BN layer to improve the pruning standard and guides the local pruning of the model according to the new standard to ensure… Show more
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