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Literature Review of Deep Network Compression
2021
Informatics
Deep networks often possess a vast number of parameters, and their significant redundancy in parameterization has become a widely-recognized property. This presents significant challenges and restricts many deep learning applications, making the focus on reducing the complexity of models while maintaining their powerful performance. In this paper, we present an overview of popular methods and review recent works on compressing and accelerating deep neural networks. We consider not only pruning
doi:10.3390/informatics8040077
fatcat:u2dzzibapnf2dbjdqkvgl3pztu