# Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method
## 5 Conclusions  
Collecting data is often very restricted due to the lack of infrastructure. It is even more restrictive when labeling data is possible only through the manufacturing of products. The goal of this paper is to demonstrate the effectiveness of making a prediction through a simple but effective method when there is small amount of labeled data available on-hand. In this study, a semi-supervised regression (SSR) coupled with simple random sampling (SRS) for the generation of synthetic data is proposed to boost the overall regression performance of DNN. Then, camera lens module classification is performed based on the improved regression model enhancing the reliability of the classification result. The performance of the proposed model is validated on five different test sets for both raw and augmented models. The proposed scheme shows that the regression performance increases by 18%, 15%, and 25% while the classification performance does by 11.3%, 9%, 1.6%, and 7.6% at best, achieving 97.7% accuracy, 98.8% recall, 75.7% specificity, and 98.8% f1-score on average. This result shows that the classification performance which is heavily influenced by the discrimination threshold value of MTF is increased with the help of improved regression. Although the overall performance has improved, it is shown in the result section of regression that it begins to degrade at some point. This is due to the error in the pseudo label and the lack of raw data making it challenging to figure out the true distribution of the data. One possible alternative to SRS would be to try some other sampling methods given that the distributions of the input variables are known. Another interesting approach would be to iterate the pseudo-labeling process a few more times until the performance ceases to get better.  
**Author Contributions:** Conceptualization, S.W.K.; writing--original draft preparation, S.W.K. and B.A.T.; writing--review and editing, S.W.K. and B.A.T.; supervision, S.L.; funding acquisition, Y.G.L. and S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by Samsung electro-mechanics, in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government Ministry of Science and ICT (MSIT)  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Data Type** & **Accuracy** & **Recall** & **Specificity** & **F1-Score** \\ \hline Augmented & 97.7\% & 98.8\% & 75.7\% & 98.8\% \\ Raw & 86.4\% & 89.8\% & 74.1\% & 91.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy, recall, specificity and f1-score for augmented and raw data.  
Figure 7: (**a**) Confusion matrix and (**c**) ROC for augmented data, (**b**) confusion matrix and (**d**) ROC for raw data.  
(No. 2020 RIA2C1009744), in part by the Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by of the Ministry of Trade, Industry and Energy (MOTIE) under Grant 10067766, in part by the Korea Institute for Advancement of Technology (KIAT) Grant funded by the Korean Government (MOTIE) (the Competency Development Program for Industry Specialist) under Grant N0008691, and in part by the Institute of Civil Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade, Industry and Energy of Korean government under grant No. 19-CM-GU-01.