## 4 Experimental Results
### ResNet on CIFAR-10
Overall, while evaluating our pruning method for ResNet on the CIFAR-10 dataset with four different depths, in the case of ResNet-20 and ResNet-32, only FPGM [15] produce slightly better results than our method and in the case of ResNet-56 and ResNet-110, our method outperforms several other popular methods in literature including FPGM [15] while pruning with similar pruning rates. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Depth** & **Method** & **Baseline Acc. (\%)** & **Pruned Acc. (\%)** & **Acc. Drop (\%)** & **FLOPs (\downarrow) (\%)** \\ \hline \multirow{4}{*}{20} & MIL [47] & 91.53 & 91.43 & **0.10** & 20.3 \\  & SFP [13] & 92.0 \(\pm\) 0.18 & 90.83 \(\pm\) 0.31 & 1.37 & 42.2 \\  & FPGM [15] & 92.20 \(\pm\) 0.18 & **91.99 \(\pm\) 0.15** & 0.21 & 54.0 \\  & MSVFP(ours) & **92.34 \(\pm\) 0.09** & 91.78 \(\pm\) 0.17 & 0.56 & **54.5** \\ \hline \multirow{4}{*}{32} & MIL [47] & 92.33 & 90.74 & 1.59 & 31.2 \\  & SFP [13] & 92.63 \(\pm\) 0.70 & 92.08 \(\pm\) 0.08 & 0.55 & 41.5 \\  & FPGM [15] & 92.63 \(\pm\) 0.70 & **92.82 \(\pm\) 0.03** & \(\mathbf{-0.19}\) & 53.2 \\  & LFPC [14] & 92.63 \(\pm\) 0.70 & 92.12 \(\pm\) 0.32 & 0.51 & 52.6 \\  & MFP [16] & 92.63 \(\pm\) 0.70 & 91.85 \(\pm\) 0.09 & 0.78 & 53.2 \\  & GFI-AP [25] & 92.54 & 92.09 \(\pm\) 0.15 & 0.45 & 42.5 \\  & MSVFP(ours) & **92.73 \(\pm\) 0.60** & 92.72 \(\pm\) 0.50 & 0.01 & **53.4** \\ \hline \multirow{4}{*}{56} & PFEC [24] & 93.04 & 93.06 & \(-\)0.02 & 27.6 \\  & SFP [13] & **93.59 \(\pm\) 0.58** & 93.35 \(\pm\) 0.31 & 0.24 & 52.6 \\  & FPGM [15] & **93.59 \(\pm\) 0.58** & 93.26 \(\pm\) 0.03 & 0.33 & 52.6 \\  & HRank [48] & 93.26 & 93.17 & 0.09 & 50.0 \\  & NPPM [49] & 93.04 & 93.40 & \(\mathbf{-0.36}\) & 50.0 \\  & LFPC [14] & **93.59 \(\pm\) 0.58** & 93.34 \(\pm\) 0.08 & 0.25 & **52.9** \\  & MFP [16] & **93.59 \(\pm\) 0.58** & 93.56 \(\pm\) 0.16 & 0.03 & 52.6 \\  & MSVFP(ours) & 93.48 \(\pm\) 0.45 & **93.59 \(\pm\) 0.11** & -0.11 & 52.7 \\ \hline \multirow{4}{*}{10} & PFEC [24] & 93.53 & 93.30 & 0.23 & 38.6 \\  & MIL [47] & 93.63 & 93.44 & 0.19 & 34.2 \\ \cline{1-1}  & SFP [13] & 93.68 \(\pm\) 0.32 & 93.86 \(\pm\) 0.21 & \(-\)0.18 & 40.8 \\ \cline{1-1}  & Rethink [50] & 93.77 \(\pm\) 0.23 & 93.70 \(\pm\) 0.16 & 0.07 & 40.8 \\ \cline{1-1}  & FPGM [15] & 93.68 \(\pm\) 0.32 & 93.85 \(\pm\) 0.11 & \(-\)0.17 & 52.3 \\ \cline{1-1}  & HRank [48] & 93.50 & 93.36 & 0.14 & 58.2 \\ \cline{1-1}  & LFPC [14] & 93.68 \(\pm\) 0.32 & 93.07 \(\pm\) 0.15 & 0.61 & **60.3** \\ \cline{1-1}  & MFP [16] & 93.68 \(\pm\) 0.32 & 93.31 \(\pm\) 0.08 & 0.37 & 52.3 \\ \cline{1-1}  & MSVFP(ours) & **93.69 \(\pm\) 0.22** & **93.92 \(\pm\) 0.52** & \(\mathbf{-0.23}\) & 52.4 \\ \hline \hline \end{tabular} Note that bold numbers in the table indicate the best result.  
\end{table} 
Table 4: Comparison of pruned ResNet models on the CIFAR-10 dataset. FLOPs (\(\downarrow\)) (%) represents, the FLOPs reduction rate between the baseline model and pruned model. “Acc. drop” is the top-1 accuracy difference between the baseline model and pruned model. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & \(w_{mag}\) & **Top-1 Acc. (\%)** \\ \hline Baseline & - & 92.34 \(\pm\) 0.09 \\ \hline Pruned (\(l_{1}\)-norm) & 1.0 & 91.72 \(\pm\) 0.15 \\ Pruned (eucl) & 0.0 & 91.61 \(\pm\) 0.27 \\ Pruned (cos) & 0.0 & 90.76 \(\pm\) 0.22 \\ Pruned (ncc) & 0.0 & 90.77 \(\pm\) 0.27 \\ Pruned (\(l_{1}\)-norm + eucl) & 0.5 & **91.78 \(\pm\) 0.17** \\ \hline \hline \end{tabular} Note that bold numbers in the table indicate the best result.  
\end{table} 
Table 3: Pruning ResNet-20 on the CIFAR-10 dataset using different filter selection criteria.