Literature Review of Deep Network Compression

Ali Alqahtani, Xianghua Xie, Mark W. Jones
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
more » ... ethods but also quantization methods, and low-rank factorization methods. This review also intends to clarify these major concepts, and highlights their characteristics, advantages, and shortcomings.
doi:10.3390/informatics8040077 fatcat:u2dzzibapnf2dbjdqkvgl3pztu