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Keyword: graph attention networks ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   GAT TransPruning: progressive channel pruning strategy combining graph attention network and transformer

   Lin, Wang and Lin · PeerJ Computer Science · 2024

   Unchecked2 claims
   Show 2 claims

   1. Unchecked“The experimental results reveal that the accuracy rate only drops by 6.58% when the channel pruning rate is 89% for VGG-19/CIFAR-100.”
   2. Unchecked“In addition, the lightweight model inference speed is 9.10 times faster than that of the original large model.”