# A Lightweight Module for Feature Fusion Based on Their Dimensions
## 4 Experiments and Result
### Module Effectiveness Verification  
To explore whether \(\varphi\)unit has the expected efficiency and portability in future application scenarios, this experiment selects the more operable VGG16 [16] as the baseline model for this experiment. For the position selection of adding or replacing the verification module, this experiment selects between different groups where both dimension and feature size change, and the whole experimental process is shown in Figure 4. To evaluate whether \(\varphi\)unit has good performance at any position in the model, this experiment adds a combination of deconvolution and \(\varphi\)unit at three positions in the baseline model, respectively, for testing; to verify the efficiency of the proposed \(\varphi\)unit compared with the conventional information fusion module, this experiment adds two extra control experiments between the dimension change from 128 to 256 in the baseline model. The relevant experimental results are shown in Table 2.  
The experimental results in Table 2 are based on the Dog VS. Cat binary classification dataset. Since the dataset has fewer classification categories, VGG16 can achieve a baseline accuracy of 97.3% on the dataset without any modification. This also indicates that, to further improve the accuracy on such a high accuracy basis, the extra replaced and added modules need to have strong feature extraction and information integration capabilities,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Channel** & **Operation** & **Acc. (\%)** \\ \hline - & - & 97.33 \\
64–128 & TransConv + \(\varphi\)unit & 97.07 \\ \hline
128–256 & TransConv + \(\varphi\)unit & **97.77** \\
128–256 & TransConv + Cat & 97.03 \\
128–256 & TransConv + Conv & 97.53 \\ \hline
256–512 & TransConv + \(\varphi\)unit & **97.51** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different connectivity handling operations are used at different dimensional variations in the VGG16 network.  
Figure 4: This figure illustrates the network for evaluating the efficiency and portability of the \(\varphi\)unit design based on VGG16. The original image information is fed into the network after being improved by VGG16 to extract feature information, and then the feature information is passed to the FC layer to obtain the classification information obtained by the network after inference. Each red horizontal connection in the diagram represents a validation experiment scheme.  
and have good feature integration in the whole model. Only in this way can the added and replaced modules make a meaningful contribution to the further performance improvement of the model. In all the improved models with added \(\varphi\)unit modules, except for the model whose overall performance declined when the feature dimension changed from 64 to 128, the performance of the improved models at other locations increased. The reason for this phenomenon is that when \(\varphi\)unit is located at a too marginal position in the network, the whole network will focus too much on acquiring the edge feature information, and the subsequent feature extraction layers will have difficulty finding the required information through too much edge information, which leads to the decline of the overall accuracy of the model. This also indirectly verifies the efficient information fusion ability of \(\varphi\)unit module. To better demonstrate the advantages of the proposed \(\varphi\)unit over the traditional information fusion modules, this experiment added different information fusion modules at the position where the feature dimension changed from 128 to 256 for comparative experiments. The experimental results show that the proposed \(\varphi\)unit performs better than the traditional information fusion methods. This also proves that the module has significant advantages in both structure and feature information fusion mode. This further confirms the effectiveness and superiority of the module in improving the model performance.