Implementation of a Hierarchical Authorization Method for Deep Neural Networks Based on Model Pruning
Based on the problem that existing Deep Neural Networks models cannot be hierarchically authorized according to the usage authority,a novel hierarchical authorization method for DNN models is designed and proposed,which can distribute different model accuracies according to different model permissions.The method realizes model performance grading based on the model pruning technique,which uses a specific pruning rate or pruning threshold to prune and fine-tune the model.By adjusting the model during the pruning and fine-tuning stages,the model outputs different levels of accuracy.Finally different user privileges are matched with the corresponding level of accuracy.Experiments are conducted on multiple datasets and DNN models and validated by using CIFAR-10 and CIFAR-100 datasets.The experimental results show that the method is effective in grading the performance of the model and works well on several DNN models.
Deep Neural Networkshierarchical authorizationcopyright protectionmodel pruning
国家科技期刊平台
NSTL
万方数据
维普
