## 4 Experimental Results
### Comparison with Methods in MVTec  
Since there are no experimental data from other literature about the classification accuracy on the nanofiber dataset, we only compare the classification accuracy of carpet and grid datasets with other methods. The classification accuracy and the accuracy of the detected defective area are analyzed by using the complete images. In this paper, we compare our proposed method with the classification method in MVTec [14]. First, MVTec [14] selects some normal images as the training set and defines the minimum number of pixels for a defective area, that is, if the defect area is less than this number of pixels, it is judged as noise. Second, to determine the classification threshold, the threshold value is increased gradually from 0 to 255. When the maximum defect area in the training images is just below the defined minimum defective area, this threshold is used as the image classification threshold. In Table 14, we compare the four proposed models with the models in [14]. In general, our proposed models outperform the models in [14] on both classifications of normal and defect images. Even if the DEC loss function is not included in the training model, the classification accuracy of defect images is higher than 80%. Among the four models, IAE + DEC has overall better performance. The main reason for improving the accuracy of image classification is that the multi-thresholding image segmentation algorithms provide adaptive thresholding for each test image to distinguish between normal and defective images. The method used in [14] sets a fixed classification threshold for each image, so it is difficult to have high classification accuracy. In Table 15, we analyze the accuracy of the detected defective area with the methods in [14]. The method of analyzing the accuracy of the detected defective area is based on the method in [14] shown in Figure 28. The green area indicates the defective area that is predicted correctly, and the red area is the defective area that is missed in the prediction. The accuracy of the detected defective area is calculated as the ratio between the correct predicted area and the union of ground truth and predicted area. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Carpet** & **Grid** \\ \hline SAE & 56\% & 58\% \\ \hline SAE + DEC & 76\% & 74\% \\ \hline IAE & 48\% & 53\% \\ \hline IAE + DEC & 78\% & 73\% \\ \hline AE(SSIM) [14] & 69\% & 88\% \\ \hline AE(L2) [14] & 38\% & 83\% \\ \hline AnoGAN [14] & 34\% & 4\% \\ \hline CNN Dict [14] & 20\% & 2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Analysis of the accuracy of detected defective area with the methods in [14]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Carpet**} & \multicolumn{2}{c}{**Grid**} \\ \hline
**Model** & **Good** & **Defect** & **Good** & **Defect** \\ \hline SAE & 100\% & 85\% & 95\% & 98\% \\ \hline SAE + DEC & 89\% & 98\% & 100\% & 100\% \\ \hline IAE & 96\% & 84\% & 100\% & 94\% \\ \hline IAE + DEC & 96\% & 98\% & 100\% & 98\% \\ \hline AE(SSIM) [14] & 43\% & 90\% & 38\% & 100\% \\ \hline AE(L2) [14] & 57\% & 42\% & 57\% & 98\% \\ \hline AnoGAN [14] & 82\% & 16\% & 90\% & 12\% \\ \hline CNN Dict [14] & 89\% & 36\% & 57\% & 33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of the four proposed models with the models in [14]. In Table 15, when the DEC loss function is not included, the highest accuracy of the detected defective area among the proposed models has a detection accuracy of only 50%. However, after the DEC loss function is included, the accuracy is increased to more than 70%, indicating that the DEC loss function improves the accuracy of the detected defective area. Among the four methods used in [14], the AE (SSIM) method is the best. The accuracy of the detected defective area for the grid dataset is 88% that is higher than our proposed models.