## 4 Results
### Ablation Study  
We use the RandAugment [39] ablation experiment methodology to validate the contribution of each transformation to the SigAugment. Table 6 shows the results. When a specific transformation is added to the transformation set, the average difference in test accuracy increases. SE-MSFN models were trained on the 2016A-10, 2016A-10a, 2016A-10b, 2016A-20, and 2016A-50 datasets with SigAugment-R for ablation study. We can see that almost all of the transformations can improve test accuracy, with the SP transformation being the most helpful for SigAugment-R. However, Table 5 shows that the Rot transformation alone achieves the top-two average recognition accuracy gains on the three datasets, whereas the gains from the Rot transform (0.80%, 1.22%, 0.86%, 0.43%, and 2.98%) for SigAugment-R are relatively small compared to the other transforms, which does not appear to meet our intuitive expectations. Several experiments, however, confirmed this. We believe that this is an open question and that the issue of the gain of individual transforms on combined enhancement merits a more in-depth investigation that is beyond the scope of this paper. Figures 11 and 12 depict in detail the effect of the number of transforms on the accuracy and stability of the SigAugment method. From the above four sets of experiments, we can see that the proposed SigAugment data augmentation methods are insensitive to hyperparameters. For different values of \(N\), the variance in the results of the model tested on two small datasets is tiny. In general, SigAugment-R uses a maximum number of transforms equal to the total number of transforms in the transformation pool, and larger \(N\) values give higher gains, while for SigAugment-C, \(N=4\) seems to be a better choice. In practice, the transformation selection mode for SigAugment can thus be chosen based on the model and data. Figure 8 depicts the top three transformations on the class-imbalanced dataset: Rot, SP, and FCS. To validate the effectiveness of the proposed method, we used the SE-MSFN to validate the performance of joint augmentation using the above three transformations on six datasets: 2016A-1, 2016A-10, 2016A-10a, 2016A-10b, 2016A-20, and 2016A-50. On all datasets, SigAugment outperforms the joint augmentation method, as shown in Table 7. Furthermore, when Tables 5 and 7 are combined, we can see that the joint augmentation method outperforms the use of individual data augmentation methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**2016A-10**} & \multicolumn{3}{c}{**2016A-10a**} & \multicolumn{3}{c}{**2016A-10b**} & \multicolumn{3}{c}{**2016A-20**} & \multicolumn{3}{c}{**2016A-50**} \\ \cline{2-13}  & **Mean** & **std** & **Gain** & **Mean** & **std** & **Gain** & **Mean** & **std** & **Gain** & **Mean** & **std** & **Gain** & **Mean** & **std** & **Gain** \\ \hline Baseline & 81.36 & 0.07 & - & 78.49 & 0.31 & - & 73.26 & 0.13 & - & 78.32 & 0.99 & - & 70.93 & 1.37 & - \\ Jit & 79.83 & 0.44 & **1.53** & _77.62 & 0.72 & 0.88 & 72.12 & 1.48 & 1.14 & 77.70 & 0.68 & 0.62 & 70.09 & 1.53 & 0.84 \\ Rot & 80.56 & 0.46 & 0.80 & 77.21 & 1.17 & 1.28 & 72.40 & 0.74 & 0.86 & 78.07 & 0.21 & 0.25 & 67.94 & 1.41 & **2.98** \\ Flip & 80.66 & 0.07 & 0.70 & _77.68_ & 0.20 & 0.81 & 72.18 & 0.61 & 1.08 & 75.23 & 4.88 & **3.09** & 70.15 & 0.61 & 0.78 \\ CS & 80.38 & 0.11 & 0.98 & 77.31 & 0.29 & 1.19 & 72.07 & 0.34 & 1.18 & 76.89 & 1.03 & 1.43 & 68.44 & 1.90 & 2.48 \\ Inv & 79.59 & 0.83 & 1.77 & 76.42 & 0.61 & **2.07** & 69.67 & 0.71 & **3.59** & 76.93 & 0.98 & 1.39 & 69.73 & 0.38 & 1.20 \\ SP & 79.19 & 0.45 & **2.17** & 75.09 & 0.60 & **3.40** & 69.50 & 0.43 & **3.75** & 73.37 & 2.79 & **4.94** & 64.46 & 1.35 & **6.47** \\ Sca & 80.54 & 0.47 & 0.82 & 78.18 & 0.59 & 0.32 & 70.89 & 0.47 & 2.37 & 78.55 & 1.01 & \(-\)0.23 & 70.39 & 0.94 & 0.54 \\ FCS & 80.50 & 0.28 & 0.86 & 77.72 & 0.29 & 0.77 & 73.26 & 0.13 & 0.96 & 78.55 & 0.63 & \(-\)0.23 & 70.69 & 0.55 & 0.24 \\ \hline \hline \end{tabular}
\end{table}