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Loss-aware automatic selection of structured pruning criteria for deep neural network acceleration
This study presents an efficient loss-aware automatic selection of structured pruning (LAASP) criteria for slimming and accelerating deep neural networks.
Magnitude and Similarity based Variable Rate Filter Pruning for Efficient Convolution Neural Networks
We studied several filter selection criteria based on filter magnitude and similarity among filters within a convolution layer, and based on the assumption that the sensitivity of each layer throughout the network is different, unlike conventional fixed rate pruning methods, our algorithm using loss-aware filter selection criteria automatically finds the suitable pruning rate for each layer throughout the network.
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