## 4 Experimental Results
### ResNet on CIFAR-10  
At first, as shown in Table 3, we test our pruning algorithm on ResNet-20 using the CIFAR-10 dataset with different filter selection criteria while reducing network FLOPs by 54.5%. Similar to VGGNet pruning (Table 1), while pruning the ResNet-20 model (Table 3), using single filter selection criteria, we found that \(l_{1}\)-norm and euclidean similarity based criteria are on the top among other filter selection criterion. Now, when we use both \(l_{1}\)-norm and euclidean similarity based criteria with equal weight the best pruning result is obtained (see last row of Table 3). Therefore, we use equally weighted \(l_{1}\)-norm and euclidean similarity based filter selection criteria (\(w_{mag}=0.5\)) to evaluate our MSVFP algorithm also on ResNet-32, ResNet-56, and ResNet-110 models with the CIFAR-10 datasets. These depths are selected to show the robustness of the proposed pruning algorithms across small to large ResNet models and are commonly used depths in the literature. Table 4 shows the effectiveness of our method in comparison with several other state-of-the-art methods. Only top-1 accuracy is shown in the table. Pruning methods such as SFP [13], FPGM [15], and MPF [16] use soft filter pruning, which means, pruning is carried out at the end of \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(w_{mag}\)} & \multicolumn{3}{c}{**Top-1 Acc (\%)**} \\ \cline{3-5}  & & **40 FT Epochs** & **80 FT Epochs** & **160 FT Epochs** \\ \hline Baseline & - & 93.79 \(\pm\) 0.23 & 93.79 \(\pm\) 0.23 & 93.79 \(\pm\) 0.23 \\ \hline Pruned & 1.0 & 93.60 \(\pm\) 0.14 & 93.71 \(\pm\) 0.14 & 93.92 \(\pm\) 0.01 \\ (\(l_{1}\)-norm) & 0.0 & 93.60 \(\pm\) 0.14 & 93.84 \(\pm\) 0.06 & 93.92 \(\pm\) 0.03 \\ Pruned (cos) & 0.0 & 93.48 \(\pm\) 0.23 & 93.64 \(\pm\) 0.12 & 93.69 \(\pm\) 0.15 \\ Pruned (ncc) & 0.0 & 93.51 \(\pm\) 0.15 & 93.69 \(\pm\) 0.27 & 93.73 \(\pm\) 0.07 \\ Pruned (\(l_{1}\)-norm & 0.5 & \(\mathbf{93.75\pm 0.06}\) & \(\mathbf{93.89\pm 0.03}\) & \(\mathbf{94.02\pm 0.04}\) \\ + eucl) & & & & \\ \hline \hline \end{tabular} Note that bold numbers in the table indicate the best result.  
\end{table} 
Table 1: Pruning VGGNet on the CIFAR-10 dataset using different filter selection criteria and a different number of fine-tune epochs. ‘FT epochs’ means ‘Fine-tune epochs’. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Baseline Top-1** & **Pruned Top-1** & **Top-1 Acc. Drop** & \multirow{2}{*}{**FLOPs (\(\downarrow\)) (\%)**} \\  & **Acc. (\%)** & **Acc. (\%)** & **Acc. (\%)** & **(\%)** \\ \hline PFEC [24] & 93.58 \(\pm\) 0.03 & 93.28 \(\pm\) 0.03 & 0.30 & 34.2 \\ FPGM [15] & 93.58 \(\pm\) 0.03 & 94.00 \(\pm\) 0.13 & \(\mathbf{-0.42}\) & 34.2 \\ MFP [16] & 93.58 \(\pm\) 0.03 & 93.76 \(\pm\) 0.08 & \(\mathbf{-0.18}\) & 34.2 \\ MSVFP(ours) & \(\mathbf{93.79\pm 0.23}\) & \(\mathbf{94.02\pm 0.04}\) & \(\mathbf{-0.23}\) & 35.1 \\ MSVFP(ours) & \(\mathbf{93.79\pm 0.23}\) & 93.84 \(\pm\) 0.08 & \(\mathbf{-0.05}\) & \(\mathbf{50.7}\) \\ \hline \hline \end{tabular} Note that bold numbers in the table indicate the best result.  
\end{table} 
Table 2: Comparison of VGGNet pruning on the CIFAR-10 dataset. each training epoch and those pruned filters are still updated in the next training epochs. It is claimed that this process is easier to retain the model performance even after pruning. In contrast, we use hard filter pruning, i.e., once the filter is selected for pruning in every pruning iteration it will be removed permanently and network connections are rearranged. The advantage is that, once pruning is completed, slimmed model requiring fewer FLOPs will go through the final fine-tuning process. Although using hard filter pruning, with 53.4% FLOPs reduction on the ResNet-32 model, the proposed MSVFP retains its baseline accuracy. Similarly, with more than 52% FLOPs reduction on ResNet-56 and ResNet-110, the proposed MSVFP even improves the baseline accuracy with margins of 0.11% and 0.23% respectively.