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This article talks about pruning in the context of convolutional neural networks:
One of the first methods of pruning is pruning entire convolutional filters. Using an L1 norm of the weight of all the filters in the network, they rank them. This is then followed by pruning the ‘n’ lowest ranking filters globally. The model is then retrained and this process is repeated.
There also exist methods for implementing structured pruning for a more light-touch approach of regulating the output of the method. This method utilizes a set of particle filters that are the same in number as the number of convolutional filters in the network.