  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.
