Modeling of Pruning Techniques for Deep Neural Networks Simplification

Autor:	Pasandi, Morteza Mousa, Hajabdollahi, Mohsen, Karimi, Nader, Samavi, Shadrokh
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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 is to provide a general approach for all of the pruning methods with different structures and applications. Comment: six pages, eight figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.04062 Zobrazit plný text záznamu View this record from Arxiv