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Table 2 Results of pruning VGG on CIFAR-10

From: An efficient pruning scheme of deep neural networks for Internet of Things applications

Method Baseline (%) Accuracy (%) FLOPs pruned (%) Parameters pruned (%)
PFEC[18] 93.25 0.15 34.2 64.0
NS[19] 93.66 0.14 51.0 88.5
CFP[32] 93.49 0.51 81.9 -
COP[33] 93.56 0.25 73.5 92.8
GAL[35] 93.96 0.54 45.2 82.2
PFS[38] 93.44 0.19 50.0 -
Ours 93.50 0.25 49.3 83.8
   0.23 72.6 94.1
  1. We report both baseline and after-pruning accuracy of the state-of-the-art methods referring to their papers. The label “-” in last column indicates that such item is not reported