## 4 Ablation Study
### Influence of Different Initialization Strategies on Performance  
To analyze the difference of the reduction in the FLOPs with the different initialization strategies, we compared the difference in the FLOPs per layer, which is noted in Table 5. Although no fixed pruning rate was used, the proposed approach showed an almost identical reduction of the FLOPs in all layers, except the last one. It can be easily observed that, with a random initialization strategy, many filters were pruned, resulting in an 84.98% reduction of the FLOPs. More specifically, more than 98% of the FLOPs were reduced from all layers, except the last layer, indicating that many weak or non-discriminative filters \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Method** & **Val. Loss** & **Val. Acc. \(\downarrow\) (\%)** & **FLOPs \(\downarrow\) (\%)** & **\# Param. \(\downarrow\) (\%)** \\ \hline \multirow{8}{*}{\(\begin{array}{c}\text{TernausNet}\\ \text{(Chaotic)}\end{array}\)} & Random & \multirow{2}{*}{0.0180} & \multirow{2}{*}{0.05} & \multirow{2}{*}{74.73} & \multirow{2}{*}{74.13} \\  & Pruning & & & & & \\ \cline{1-1} \cline{5-6}  & \(l_{1}\)-Norm & 0.1491 & & & & \\ \cline{1-1} \cline{5-6}  & Entropy & 0.0174 & & & & \\ \cline{1-1} \cline{5-6}  & Proposed & \multirow{2}{*}{0.0190} & \multirow{2}{*}{0.05} & \multirow{2}{*}{84.34} & \multirow{2}{*}{83.50} \\  & (Random) & & & & & \\ \cline{1-1} \cline{5-6}  & Proposed & \multirow{2}{*}{0.0190} & \multirow{2}{*}{0.07} & \multirow{2}{*}{77.37} & \multirow{2}{*}{81.36} \\  & (Chaotic) & & & & & \\ \cline{1-1} \cline{5-6}  & Proposed & \multirow{2}{*}{0.0181} & \multirow{2}{*}{0.05} & \multirow{2}{*}{75.84} & \multirow{2}{*}{75.42} \\  & ([PERSON]) & & & & & \\ \cline{1-1} \cline{5-6}  & Proposed & \multirow{2}{*}{0.0178} & \multirow{2}{*}{0.03} & \multirow{2}{*}{74.96} & \multirow{2}{*}{74.27} \\  & ([PERSON]) & & & & & \\ \hline \multirow{8}{*}{U-Net} & Random & \multirow{2}{*}{0.0184} & \multirow{2}{*}{0.04} & \multirow{2}{*}{75.84} & \multirow{2}{*}{75.42} \\  & Pruning & & & & & \\ \cline{1-1} \cline{5-6}  & Entropy & 0.0170 & & & & \\ \cline{1-1} \cline{5-6}  & Proposed & \multirow{2}{*}{0.0178} & \multirow{2}{*}{0.06} & \multirow{2}{*}{81.46} & \multirow{2}{*}{80.75} \\  & (Chaotic) & & & & & \\ \cline{1-1} \cline{5-6}  & Proposed & \multirow{2}{*}{0.0170} & \multirow{2}{*}{0.02} & \multirow{2}{*}{76.13} & \multirow{2}{*}{75.82} \\  & ([PERSON]) & & & & & \\ \cline{1-1} \cline{5-6}  & Proposed & \multirow{2}{*}{0.0173} & \multirow{2}{*}{0.03} & \multirow{2}{*}{75.41} & \multirow{2}{*}{74.92} \\  & ([PERSON]) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with the state-of-the-art methods for pruning TeranusNet and U-Net on AIRS dataset. “\(\downarrow\)” represents the reduction rate. were removed. The chaotic initialization also showed similar performance. Despite He, Lecun, and Xavier initialization resulting in a larger number of pruned filters, the reduction in the FLOPs and network parameters was still acceptable. We firmly believe that a random initialization strategy works best among all initialization strategies used in the proposed approach because of its precise ability to prune less discriminant or weak filters. In effect, more filters are pruned with a random initialization than with other initialization strategies without losing much accuracy. Table 6 reports the compression and acceleration analysis of the proposed approach under different initialization strategies on TernausNet. The compression ratio tells how much the network is compressed and is computed by dividing the number of parameters of the baseline network by that of the pruned network. The acceleration ratio reflects the theoretical computational costs and is computed by dividing the FLOPs of the baseline network by those of the pruned network. As reported in Table 6, a random initialization strategy achieved the highest acceleration, as well as the highest compression.