## 3 Experiments
### Experiments on Inria Dataset
#### 3.3.2. U-Net on Inria  
U-Net is a U-shaped architecture consisting of encoder-decoder paths [62]. The encoder captures contextual or low-resolution features from input data while reducing the spatial dimensions (downsampling) and increasing the channels. The pre-trained U-Net (baseline model) has 54.6 B FLOPs and 31 M parameters with 96.13% Val. Acc. We can observe that the proposed approach strongly reduced the number of FLOPs while keeping a similar accuracy and a comparable reduction in the number of parameters. As shown  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Network** & **Model** & **Val. Loss** & **Val. Acc. (\%)** & **Val. Acc. \(\downarrow\) (\%)** & **FLOPs** & **FLOPs\(\downarrow\) (\%)** & **\# Param.** & \# Param. \(\downarrow\) (\%)** \\ \hline \multirow{8}{*}{\begin{tabular}{} \end{tabular} } & Baseline & 0.1037 & 96.00 & - & 32.3 B & - & 22.9 M & - \\ \cline{2-8}  & \begin{tabular}{c} Proposed \\ (Random) \\ \end{tabular} & 0.1121 & 95.68 (\(\pm\)0.001) & 0.32 & 4.85 B & 84.98 & 3.77 M & 83.54 \\ \cline{2-8}  & \begin{tabular}{c} Proposed \\ (Chaotic) \\ \end{tabular} & 0.1061 & 95.91 (\(\pm\)0.001) & 0.09 & 6.27 B & 80.59 & 4.33 M & 81.12 \\ \cline{2-8}  & \begin{tabular}{c} Proposed \\ (He) \\ \end{tabular} & 0.1046 & 96.04 (\(\pm\)0.001) & \(-\)0.04 & 11.6 B & 64.18 & 6.14 M & 73.20 \\ \cline{2-8}  & \begin{tabular}{c} Proposed \\ ([PERSON]) \\ \end{tabular} & 0.1046 & 96.00 (\(\pm\)0.001) & 0.00 & 11.0 B & 66.02 & 6.36 M & 72.25 \\ \cline{2-8}  & \begin{tabular}{c} Proposed \\ ([PERSON]) \\ \end{tabular} & 0.1039 & 96.04 (\(\pm\)0.001) & \(-\)0.04 & 11.6 B & 64.18 & 6.14 M & 73.20 \\ \hline \multirow{8}{*}{\begin{tabular}{} \end{tabular} } & Baseline & 0.1025 & 96.08 & - & 54.6 B & - & 31.0 M & - \\ \cline{2-8}  & \begin{tabular}{c} Proposed \\ (Random) \\ \end{tabular} & 0.1079 & 95.80 (\(\pm\)0.001) & 0.27 & 8.85 B & 83.80 & 3.10 M & 83.68 \\ \cline{2-8}  & \begin{tabular}{c} Proposed \\ (Chaotic) \\ \end{tabular} & 0.1066 & 95.88 (\(\pm\)0.001) & 0.21 & 10.2 B & 81.28 & 5.06 M & 81.15 \\ \cline{2-8}  & \begin{tabular}{c} Proposed \\ ([PERSON]) \\ \end{tabular} & 0.1070 & 95.89 (\(\pm\)0.001) & 0.20 & 13.6 B & 75.17 & 5.85 M & 75.32 \\ \cline{2-8}  & \
\begin{tabular}{c} Proposed \\ ([PERSON]) \\ \end{tabular} & 0.1042 & 96.04 (\(\pm\)0.001) & 0.04 & 14.0 B & 74.46 & 7.66 M & 74.70 \\ \cline{2-8}  &
\begin{tabular}{c} Proposed \\ ([PERSON]) \\ \end{tabular} & 0.1056 & 95.94 (\(\pm\)0.001) & 0.15 & 13.9 B & 74.59 & 7.85 M & 74.39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of pruning TeranusNet and U-Net on Inria dataset. “Baseline” represents the normal training results, i.e., without pruning. “\(\downarrow\)” represents the reduction rate, which is the drop between the performance of the pruned model and the baseline model. A negative value in “Val. Acc. \(\downarrow\)” specifies an improvement in the model accuracy. B = billion, M = million.  
in Table 1, as expected, a random initialization strategy reduced the FLOPs more strongly than any other initialization strategy, whereas the drop in the Val. Acc. was negligible. When compressing U-Net with a chaotic initialization strategy, the FLOPs were reduced by 81.28%. However, the drop in accuracy was only 0.21%, which is slightly better than a random initialization strategy. The smallest drop in the Val. Acc. by just 0.04% was shown under the Lecun initialization strategy. However, the FLOPs were only reduced by 74.46%, which is 9.34% less than with a random initialization strategy, indicating many important or discriminative filters were removed. Moreover, the error performance of the pruned models was also comparable to that of the baseline models.  
Based on the performance on TeranusNet and U-Net, we can say that our pruning approach can achieve promising results under all initialization strategies and obtain a compact network model.