## 5 Conclusions  
In this paper, a novel filter pruning method was proposed to obtain a compact and lightweight CNN model. In the proposed filter pruning approach, importance weights were applied to each convolutional filter, which helped select those filters, which learned more efficiently and contributed more effectively. We analyzed various weight initialization strategies to discard weak filters. Our approach ensures that the pruned model will not cross the pre-defined error tolerance level. Extensive experiments on various segmentation models such as U-Net and TernausNet demonstrated the excellent performance of the proposed filter pruning approach. Furthermore, we confirmed that our approach achieved better performance than other state-of-the-art approaches.  
Based on the overall performance, we can conclude that our proposed approach can obtain promising results under all initialization strategies with both small and large datasets. The experimental results on two segmentation models and two datasets indicated that our approach can obtain very compact pruned models with better performance than state-of-the-art approaches. However, iteratively checking the loss difference between the unpruned and pruned model took some time. Furthermore, we used two hyperparameters in our study, and selecting the appropriate ones is crucial. Therefore, in the future, we will use a method that works faster and use a method to automatically determine the parameters based on network models and datasets. Additionally, in the future, we would like to explore the benefits of the proposed pruning method for classification tasks on larger datasets, such as ImageNet.  
[PERSON] and T.G.: formulated the research goal, designed the methodology, and wrote the main manuscript text; M.W.: implemented and executed all the experiments; S.G.: prepared Figures 1 and 3 and data curation; N.H.: planned and coordinated the research activity; E.W.L.: analyzed the results. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received financial support for research and publication from European Research Consortium for Informatics and Mathematics (ERCIM) fellowship program.  
Not applicable.  
The authors would like to thank the Fraunhofer Institute for Integrated Circuits (IIS) for providing the infrastructure for carrying out this research work and the European Research Consortium for Informatics and Mathematics (ERCIM) for the award of a Research Fellowship.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Mean of Weights**} & \multicolumn{3}{c}{**1.2-Times the Mean of Weights**} \\ \hline  & **Val. Acc. (\%)** & **FLOPs \(\downarrow\) (\%)** & **\# Param. \(\downarrow\) (\%)** & **Val. Acc. (\%)** & **FLOPs \(\downarrow\) (\%)** & **\# Param. \(\downarrow\) (\%)** \\ \hline Random & 95.92 & 74.78 & 74.29 & 95.76 & 84.34 & 83.50 \\ \hline Chaotic & 95.93 & 74.43 & 74.72 & 95.83 & 80.59 & 81.12 \\ \hline He & 95.82 & 77.19 & 75.50 & 95.79 & 77.30 & 75.50 \\ \hline LeCun & 95.91 & 74.78 & 74.29 & 95.93 & 74.96 & 74.31 \\ \hline [PERSON] & 95.79 & 77.19 & 75.50 & 95.82 & 77.30 & 75.50 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Pruning results for TernausNet on Inria dataset under various threshold values.