## 5 Case Study
### Comparison with Other Models
#### 5.4.2 Comparison with the Model Obtained by a Black Box Trial Method  
To verify the advantages of the MTGDI-CNN model based on the DARTS method proposed in this study compared with the model obtained by the ordinary black box method, this study compared the proposed model with the model obtained via the black-box enumeration method proposed by [PERSON] et al. (2014) (hereafter referred to as the [PERSON] model).  
The [PERSON] model is built by constructing a functional layer (FL) with one ordinary 2D convolution operation, one ReLU, and one 2D MaxPool, and then simply combining multiple FLs and specifying the 2D convolution kernel size of different layers. The architecture of the [PERSON] model can be determined by assigning four hyperparameters: FL layer number, the initial size of convolution kernel, minimum width, and the minimum height  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **CAS Train** & **CAS Test** & **FMV Train** & **FMV Test** \\ \hline M-3-1D & ORI & ORI & ROS & ORI \\ BASE-1D & ORI & ORI & ORI & ORI \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset processing mode settings for different strategies of 1D model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **Avg. \(F_{2}^{\text{TGD}}\)** & **Avg. \(F_{1}^{\text{TGD}}\)** & **Avg. \(F_{0.5}^{\text{TGD}}\)** & **Avg. Training Time (GPU Days)** \\ \hline M-3 & **0.8684** & **0.8428** & **0.8187** & 0.0655 \\ M-3-1D & 0.6516 & 0.6482 & 0.6456 & 0.0648 \\ BASE-1D & 0.5286 & 0.5613 & 0.5997 & 0.0238 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model performance evaluation results of 1D model.  
of the feature maps. Owing to certain limitations, only 360 models were available with different hyperparameter combinations, and [PERSON] et al. [14] used the black-box enumeration method to search the architecture with superior performance. For comparison, the search space size was 8 ~ 14 in the proposed method.  
Enumerated training and validation of the [PERSON] models with 360 architectures were performed under three different random seeds, utilizing the same computational resources and dataset as mentioned in the previous section. The batch size of each iteration was 512, and the number of training epochs was 100. Under three random seeds, the process required an average of 2.1325 GPU days. In comparison, the proposed model (M-3) required an average time of 0.3474 GPU days, which was 83.6% lower. Subsequently, the average values of \(F_{2}^{\text{TCD}}/F_{1}^{\text{TGD}}/F_{0.5}^{\text{TGD}}\) of the 360 models under three random seeds were calculated. The maximum value of each average metric value was selected and compared with the results of the MTGDI-CNN (values of M-3 in Table 3), as summarized in Table 6. The performance of MTGDI-CNN was remarkably superior to that of the [PERSON] models, and the three performance metrics were improved by 6.7, 2.7, and 0.1%. Thus, the proposed method can significantly improve the efficiency of the model architecture search compared with the common black-box enumeration method, and the MTGDI-CNN offers superior comprehensive performance.