# Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network
## 5 Conclusions  
In this work, we found that there is redundant information in the dynamic convolutional network with an attention mechanism. To solve this problem, we proposed an effective dynamic convolutional network, EAM-DyNet, which can reduce the redundancy of spatial information by reducing the number of channels in the feature map and extract useful spatial information. Then, due to the loss of useful information when downsampling the information, it uses an adaptive average pooling method to maintain the information  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**The Selection of Pooling**} & **CIFAR10** & **CIFAR100** & **ImageNet100** \\ \cline{2-4}  & **Top-1 (\%)** & **Top-1 (\%)** & **Top-1 (\%)** \\ \hline DyNet MobilenetV1 \(\times\) 1.0 & 93.99 & 74.76 & 75.94 \\ \hline EAM-DyNet MobilenetV1 \(\times\) 1.0 (AdaptAvgPool) & 93.61 & 74.83 & 76.37 \\ EAM-DyNet MobilenetV1 \(\times\) 1.0 (AdaptMaxPool) & 93.89 & 74.91 & 76.50 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The accuracy rate when using different pooling on EAM-DyNet MobilenetV1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Network**} & **ImageNet100** & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**FLOPs**} \\  & **Top-1 (\%)** & & \\ \hline EAM-DyNet MobilenetV1 \(\times\) 1.0 (1/4C) & 76.58 & 13.48M & 585.17M \\ EAM-DyNet MobilenetV1 \(\times\) 1.0 + RCR & 76.37 & **8.98M** & **583.55M** \\ EAM-DyNet MobilenetV1 \(\times\) 1.0 + CGR & 76.11 & **8.98M** & **583.55M** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The parameters and FLOPs of different channel selection methods on EAM-DyNet MobilenetV1.  
integrity. The effectiveness of our model was verified through experiments on different datasets and different baseline networks. The results showed that EAM-DyNet not only increased the accuracy, but also reduced the number of network parameters and FLOPs compared to the existing approaches.  
Conceptualization, Y.C. and Z.L.; methodology, Y.C. and Z.L.; software, Y.C. and Z.L.; validation, Y.C. and Z.L.; formal analysis, C.X. and Z.L.; investigation, Y.C. and C.X.; resources, Y.C.; data curation, Y.C. and Z.L.; writing--original draft preparation, Y.C.; writing--review and editing, C.X., Z.L. and W.Y.; visualization, Y.C. and Z.L.; supervision, E.D.; project administration, E.D.; funding acquisition, E.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on On-line identification of Coal Gangue Based on terahertz detection technology (grant number NO.52074273), and by the State Key Research Development Program of China (grant number NO. 2017 YFC0804400, NO. 2017 YFC0804401).  
Restrictions apply to the availability of these data. Data was obtained from [third party] and are available [from the authors/at URL] with the permission of [third party].  
This work is supported in part by Research on On-line identification of Coal Gangue Based on terahertz detection technology (NO. 52074273), and in part by the State Key Research Development Program of China under Grant (NO. 2017 YFC0804400, NO. 2017 YFC0804401).  
The authors declare no conflict of interest.