## 4 Results
### Ablation Experiments  
In this paper, we also conducted ablation experiments to validate the effectiveness of the individual modules. The use of each module is indicated by \(\checkmark\), while its absence is denoted by \(\times\). All three modules generated images of size 256 \(\times\) 256 \(\times\) 3. Table 3 presents a subset of the original defect images, while Table 5 displays the synthesized images of different defects (i.e., BAF, MAF, and LCC) under different modules. The first three rows show images generated with one module removed, while the last three rows display images generated with two modules removed. Overall, the image quality remained acceptable, but images generated using the SA module exhibited a higher coherence in layout and better alignment with the original images, enhancing their similarity. According to Table 5, the image quality was slightly lower when only the SN and TTUR modules were used compared to when only the SA module was included. This is expected, as the SN module stabilizes the training process but does not improve the image details. Table 6 presents a comparative analysis of the training losses of the generator and discriminator under the different module configurations, and discriminator loss comparisons under the different modules. In Table 6, the loss curves of the generator and discriminator are represented by the blue and orange lines, respectively. Models with the SN module exhibited less loss fluctuation, indicating enhanced training stability. The TTUR module, by using a smaller learning rate for the discriminator and a larger one for the generator, improves the data distribution representation and adversarial learning stability, leading to better generator quality and reduced loss. The generator's loss value varied the most, suggesting that the model effectively leveraged the capabilities of the SA, SN, and TTUR modules to enhance the image quality. When one module was removed, as shown in the first three rows, removing the SN module had a minor impact on the image quality and training stability. However, when two modules were removed, as demonstrated in the last three rows, the decline in image quality and training stability was more pronounced. Specifically, removing both the SA and SN modules led to a significant deterioration in visual quality and training instability, characterized by large fluctuations in training loss (Table 6). Removing the SA and TTUR modules resulted in a substantial drop in image quality due to the lack of key image feature capture and optimized learning rate adjustment (Table 5). \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Description**} & \multicolumn{3}{c}{**FID (Avg)**} & \multicolumn{3}{c}{**Inception Score (Avg)**} \\ \cline{3-8}  & & **GAN** & **DCGAN** & **SAITI-DCGAN** & **GAN** & **DCGAN** & **SAITI-DCGAN** \\ \hline BAF & broken aluminum foil & 0.050169 & 0.028795 & **0.025064** & 0.567541 & 0.433728 & **0.606708** \\ MAF & missing aluminum foil & 0.044233 & 0.02598 & **0.025588** & 0.373805 & 0.56516 & **0.590326** \\ LCC & loose and crooked caps & 0.22409 & 0.028959 & **0.019037** & 0.048396 & 0.306051 & **0.336719** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the different models for the three defects.