2 research outputs found
Modeling of Pruning Techniques for Deep Neural Networks Simplification
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 figure
Modeling Neural Architecture Search Methods for Deep Networks
There are many research works on the designing of architectures for the deep
neural networks (DNN), which are named neural architecture search (NAS)
methods. Although there are many automatic and manual techniques for NAS
problems, there is no unifying model in which these NAS methods can be explored
and compared. In this paper, we propose a general abstraction model for NAS
methods. By using the proposed framework, it is possible to compare different
design approaches for categorizing and identifying critical areas of interest
in designing DNN architectures. Also, under this framework, different methods
in the NAS area are summarized; hence a better view of their advantages and
disadvantages is possible.Comment: 6 pages, 7 figure