## 3 Experiments
### Experiments on AIRS Dataset
#### 3.4.1 TernausNet on AIRS  
As Table 3 corroborates, the reduction in the FLOPs and the number of network parameters is clearly obvious using the proposed approach under different initialization strategies. For instance, when compressing TernausNet with the proposed approach using a random initialization, the FLOPs were reduced by 84.34% and the parameters were decreased by 83.50%, while the drop in accuracy stayed moderate with 0.05%. In contrast, He initialization showed the same drop in accuracy with a 75.84% reduction in the FLOPs, which is 8.5% less than in the case of a random initialization strategy, indicating that many still important or discriminative filters were removed. A LeCun initialization showed an almost similar performance to that of a He initialization with a little larger reduction in the  
\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}{*}{TernausNet} & Random & \multirow{2}{*}{0.1059} & \multirow{2}{*}{0.09} & \multirow{2}{*}{74.30} & \multirow{2}{*}{75.08} \\  & Pruning & & & & \\ \cline{1-1} \cline{3-5}  & \(l_{1}\)-Norm & & & & \\ \cline{1-1} \cline{3-5}  & Entropy & 0.1066 & 0.07 & 71.87 & 69.87 \\ \cline{1-1} \cline{3-5}  & Proposed & \multirow{2}{*}{0.1121} & \multirow{2}{*}{0.32} & \multirow{2}{*}{84.98} & \multirow{2}{*}{83.54} \\  & (Random) & & & & \\ \cline{1-1} \cline{3-5}  & Proposed & \multirow{2}{*}{0.1061} & \multirow{2}{*}{0.09} & \multirow{2}{*}{80.59} & \multirow{2}{*}{81.12} \\  & (Chaotic) & & & & \\ \cline{1-1} \cline{3-5}  & Proposed & \multirow{2}{*}{0.1046} & \multirow{2}{*}{0.04} & \multirow{2}{*}{64.18} & \multirow{2}{*}{73.20} \\  & (Chaotic) & & & & \\ \cline{1-1} \cline{3-5}  & Proposed & \multirow{2}{*}{0.1046} & \multirow{2}{*}{0.00} & \multirow{2}{*}{66.02} & \multirow{2}{*}{72.25} \\  & (Lecun) & & & & \\ \cline{1-1} \cline{3-5}  & Proposed & \multirow{2}{*}{0.1039} & \multirow{2}{*}{0.04} & \multirow{2}{*}{64.18} & \multirow{2}{*}{73.20} \\ U-Net & (Xavier) & & & & \\ \hline \multirow{8}{*}{U-Net} & Random & \multirow{2}{*}{0.1055} & \multirow{2}{*}{0.15} & \multirow{2}{*}{74.21} & \multirow{2}{*}{74.19} \\  & Pruning & & & & \\ \cline{1-1} \cline{3-5}  & \(l_{1}\)-Norm & & & & \\ \cline{1-1} \cline{3-5}  & Entropy & \multirow{2}{*}{0.1062} & \multirow{2}{*}{0.12} & \multirow{2}{*}{68.79} & \multirow{2}{*}{65.94} \\  & (Lecun) & & & & \\ \cline{1-1} \cline{3-5}  & Proposed & \multirow{2}{*}{0.1079} & \multirow{2}{*}{0.28} & \multirow{2}{*}{83.80} & \multirow{2}{*}{83.68} \\ U-Net & (Xavier) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with the state-of-the-art methods for pruning TernausNet and U-Net on Inria dataset. “# Param.” indicates number of parameters, and “\(\downarrow\)” represents the reduction rate.  
FLOPs. When compressing TeranusNet with a chaotic initialization strategy, the FLOPs were reduced by 77.37%. However, the drop in accuracy amounted to 0.07%, which is almost the same as that of a random initialization strategy. Additionally, using Xavier initialization, our approach reduced the FLOPs by 75.84% and the number of parameters by 75.42% with just a 0.04% drop in accuracy. However, the reduction in the FLOPs was 8.5% less than with a random initialization strategy. Overall, the FLOPs were significantly reduced while maintaining the baseline performance, which indicates that the proposed approach is able to reduce the FLOPs by a large amount with all initialization strategies, while maintaining the Val. Acc. almost intact.