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Table 3 Results of pruning ResNet on CIFAR-10

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

Architecture Method Baseline (%) Accuracy (%) FLOPs pruned (%) Parameters pruned (%)
ResNet-32 SFP[37] 92.63 0.55 41.5 -
  FPGM[45] 92.63 0.70 53.2 -
  COP[33] 92.64 0.67 53.9 57.5
  Ours 93.20 0.21 33.0 31.7
    0.70 49.0 60.1
ResNet-56 PFEC[18] 93.04 0.02 27.6 13.7
  CFP[32] 93.57 0.25 61.5 -
  DCP[30] 93.80 0.31 50.3 50.7
  PFS[38] 93.23 0.18 50.0 -
  ASS[39] 93.26 0.03 54.1 54.2
  GAL[35] 93.26 1.68 60.2 65.9
  Ours 93.65 0.08 35.0 41.1
    0.17 49.6 58.0
    0.51 56.7 62.2