English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Convolutional Neural Networks (CNNs) suffer from different issues, such ascomputational complexity and the number of parameters. In recent years pruningtechniques are employed to reduce the number of operations and model size inCNNs. Different pruning methods are proposed, which are based on pruning theconnections, channels, and filters. Various techniques and tricks accompanypruning methods, and there is not a unifying framework to model all the pruningmethods. In this paper pruning methods are investigated, and a general modelwhich is contained the majority of pruning techniques is proposed. Theadvantages and disadvantages of the pruning methods can be identified, and allof them can be summarized under this model. The final goal of this model is toprovide a general approach for all of the pruning methods with differentstructures and applications.

Modeling of Pruning Techniques for Deep Neural Networks Simplification