Pruning (artificial neural network): Difference between revisions

Revision as of 08:56, 21 October 2023

Revision as of 04:42, 4 February 2024

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== Node (neuron) pruning ==

A basic algorithm for pruning is as follows:<ref>Molchanov, P., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2016). ''Pruning convolutional neural networks for resource efficient inference''. arXiv preprint arXiv:1611.06440.</ref><ref>[https://jacobgil.github.io/deeplearning/pruning{{Cite web |last=Gildenblat |first=Jacob |date=2017-deep06-learning23 |title=Pruning deep neural networks to make them fast and small] |url=http://jacobgil.github.io/deeplearning/pruning-deep-learning |url-status=live |access-date=2024-02-04 |website=Github |language=en}}</ref>

#Evaluate the importance of each neuron.

#Rank the neurons according to their importance (assuming there is a clearly defined measure for "importance").