Pruning (artificial neural network): Difference between revisions

Revision as of 17:27, 8 June 2021

Revision as of 17:27, 8 June 2021

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In the context of [[artificial neural network]], '''pruning''' is the practice of removing [[Parameter|parameters]] (which may entail removing individual parameters, or parameters in groups such as by [[artificial neurons|neurons]]) from an existing network.<ref>{{cite arxiv|last1=Blalock|first1=Davis|last2=Ortiz|first2=Jose Javier Gonzalez|last3=Frankle|first3=Jonathan|last4=Guttag|first4=John|date=2020-03-06|title=What is the State of Neural Network Pruning?|class=cs.LG|eprint=2003.03033}}</ref> The goal of this process is to maintain accuracy of the network while increasing its [[efficiency]]. This can be done to reduce the [[Computational resource|computational resources]] required to run the neural network.