## 3 Experiments and Discussion
### Ablation Study for Momentum Contrastive Learning  
To independently evaluate the performance of momentum contrastive learning pretraining, we compared the classification performance of the query encoder with and without momentum contrastive learning pretraining. After pretraining, we attached two linear layers as a classification head to the end of the query encoder, and the classification results were obtained by softmax activation. Unlike the overall framework, we did not use the prototypical network as the classification mechanism during fine-tuning. For the case of using momentum contrastive learning pretraining, we pretrained the model using 500,000 unlabeled data with momentum contrastive learning. Then, we fine-tuned the classification model using the training set of the nine defect types labeled data, followed by testing the classification accuracy on the test set. For the case of not using momentum contrastive learning pretraining, we directly trained the classification model using the training set of the labeled dataset. Then, we tested the classification performance on the test set.  
Table 3 shows the classification results with and without momentum contrastive learning pretraining. To verify the performance of momentum contrastive learning pretraining in the case of a small number of labeled WBMs, we also compared the classification performance when the number of labeled WBMs was reduced to 20%, 15%, and 10%, respectively. From Table 3, it can be seen that when using all labeled training sets, the recall of the classification model after momentum contrastive learning pretraining and fine-tuning was 85%. In contrast, the accuracy of the model trained directly without momentum contrastive learning pretraining was 80.7%, indicating that momentum contrastive learning pretraining improved the classification recall by 5%. When the number of labeled training samples was reduced to 20%, 15%, and 10%, the momentum contrastive learning increased the classification recall by 3%, 9%, and 3%, respectively, indicating that it significantly improved the performance in the case of a small number of labeled data.  
To visualize the improvement of using momentum contrastive learning, we plotted the results from Table 3 as a line chart, presented in Figure 9. This result also shows a significant improvement in classification performance when using momentum contrastive learning, which is observed even in the case of a reduced number of labeled WBMs. Moreover, it can be seen in Figure 9 that the classification performance of fine-tuning 10% data after momentum contrastive learning is about the same as training with 20% data without momentum contrastive learning. This indicates that using momentum contrastive learning significantly reduces the requirement of manual labeling and offers immense practical value for practice applications.  
In order to accomplish higher accuracy, it is better to perform automatic tuning algorithms such as grid search and Bayesian optimization for hyperparameters. However, because momentum contrastive learning is computationally intensive and our computing power is limited, the speeds of grid search and Bayesian optimization are prolonged. If the computing power allows, it is recommended to use grid search or Bayesian optimization algorithms to select better hyperparameters to improve the accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Percentage of Labeled Data** & **With Pretraining** & **Without Pretraining** \\ \hline
10\% & 0.68 & 0.650 \\
15\% & 0.73 & 0.650 \\
20\% & 0.782 & 0.751 \\ All (100\%) & 0.850 & 0.807 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification recall comparison for with and without momentum contrastive pretraining.