Modeling of Pruning Techniques for Simplifying Deep Neural Networks
Paper ID : 1063-MVIP2020
Morteza Mousa-Pasandi *1, Mohsen Hajabdollahi2, Nader Karimi3, Shadrokh Samavi3
2Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156-83111, Iran
3Isfahan University of Technology
Convolutional Neural Networks (CNNs) suffer from different issues such as computational complexity and the number of parameters. In recent years pruning techniques are employed to reduce the number of operations and model size in CNNs. Different pruning methods are proposed, which are based on pruning the connections, channels, and filters. Various techniques and tricks accompany pruning methods, and there is not a unifying framework to model all the pruning methods. In this paper pruning methods are investigated, and a general model which is contained the majority of pruning techniques is proposed. The advantages and disadvantages of the pruning methods can be identified and all of them can be summarized under this model. The final goal of this model can be providing a specific method for all the pruning methods with different structures and applications.
Convolutional Neural Networks, Neural Network Simplification, model Size reduction, Pruning
Status : Paper Accepted (Oral Presentation)