# Reverse Knowledge Distillation with Two Teachers for Industrial Defect Detection
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Category** & **US [14]** & **SPADE [21]** & **PaDIM [5]** & **Dataset [58]** & **NSA [7]** & **CFLOW-AD** & **UniAD [50]** & **ADRD [15]** & **Ours** \\ \hline \multirow{4}{*}{Texture} & Carpet & 93.5 & 97.5 & **99.1** & 98.3 & 96.2 & 95.5 & **99.23** & 98 & 98.9 & 99.1 \\  & Cold & 89.9 & 92.7 & 97.3 & 97.5 & 98.5 & 99.2 & 96.89 & 94.6 & 99.3 & 99.2 \\  & Lighter & 97.8 & 97.6 & 99.2 & 99.5 & 98.9 & 99.5 & 99.41 & 98.3 & 99.4 & 99.3 \\  & Tile & 92.5 & 87.4 & 94.1 & 90.5 & **99.5** & 99.3 & 97.71 & 91.8 & 95.6 & 95 \\  & Wood & 92.1 & 88.5 & 94.9 & 95.5 & **97** & 90.7 & 94.49 & 95.3 & 94.7 \\  & Average & 93.2 & 92.9 & 96.9 & 96.3 & 98.2 & 96.8 & 97.60 & 97.8 & 97.7 & 97.46 \\ \hline \multirow{4}{*}{Object} & Rottle & 97.8 & 98.4 & 98.3 & 97.6 & 99.3 & 98.3 & 98.76 & 98.1 & 98.7 & 98.6 \\  & Cable & 91.9 & 97.2 & 96.7 & 90.4 & 95.4 & 96.4 & 97.4 & 98.5 & 97.4 & 98.8 \\  & Capsule & 96.8 & 99.0 & 98.5 & 97.4 & 94.1 & 97.6 & **98.98** & 97.9 & 98.7 & 98.8 \\  & Haefauraur & 95.2 & 99.1 & 98.2 & 97.3 & 99.5 & 97.6 & 98.2 & 98.8 & 98.9 & 99.9 \\  & Metalnut & 97.2 & 98.1 & 97.2 & 98.1 & 98.7 & 98.4 & 98.6 & 95.7 & 97.3 & 97.8 \\  & Pull & 96.5 & 95.7 & 95.7 & 97.6 & 98.5 & 96.95 & 95.1 & 98.2 & 97.7 \\  & Screw & 97.4 & 98.9 & 98.5 & 96.7 & 98.7 & 96.5 & 97.4 & 99.6 & 99.6 \\  & Toothbrush & 97.9 & 97.9 & 98.8 & 98.1 & 98.1 & 94.9 & 98.6 & 97.8 & 99.1 & **99.1** \\  & Transistor & 73.7 & 94.1 & 97.3 & 93.0 & 90.0 & 88 & 93.2 & 98.7 & 99.2 & **99.7** \\  & Zipper & 95.6 & 96.5 & 95.3 & 95.6 & 94.2 & 98.4 & 96.0 & 95.2 & **99.1** \\  & Average & 94.6 & 97.6 & 97.8 & 95.7 & 97.1 & 96 & 98.14 & 97 & 97.86 & **95.27** \\ \hline \multicolumn{2}{c}{Average} & 93.9 & 96.5 & 97.5 & 96 & 97.5 & 96.3 & **97.87** & 97.4 & 97.78 & **97.87** \\ \hline \end{tabular}
\end{table} 
Table 2: Pixel-level anomaly detection results on MVTec AD dataset. Methods achieved for the top one ROC-AUC (%) are highlighted in bold. (Note: added reference numbers and 2 latest methods in table, one of which is SOTA method). \begin{table}
\begin{tabular}{c c c c c} \hline
**Category** & **US [14]** & **PaDim [35]** & **ADRD [15]** & **Ours** \\ \hline \multirow{4}{*}{Texture} & Carpet & 87.9 & 96.2 & 97 & **97.4** \\  & Grid & 95.2 & 94.6 & **97.6** & 97.5 \\  & Leather & 94.5 & 97.8 & 99.1 & **99.2** \\  & Tile & **94.6** & 86 & 90.6 & 89.7 \\  & Wood & 91.1 & 91.1 & 90.9 & **92** \\  & Average & 92.7 & 93.2 & 95.0 & **95.16** \\ \hline \multirow{4}{*}{Object} & Bottle & 93.1 & 94.8 & **96.6** & **96.6** \\  & Cable & 81.8 & 88.8 & 91.0 & **94.1** \\  & Capsule & **96.8** & 93.5 & 95.8 & 96.5 \\  & Hazelnut & **96.5** & 92.6 & 95.5 & 95.8 \\  & Metalnut & **94.2** & 85.6 & 92.3 & 92.3 \\  & Pill & 96.1 & 92.7 & **96.4** & 95.4 \\  & Screw & 94.2 & 94.4 & 98.2 & **98.5** \\  & Toothbrush & 93.3 & 93.1 & **94.5** & 94.1 \\  & Transistor & 66.6 & **84.5** & 78.0 & 81 \\  & Zipper & 95.1 & 95.9 & 95.4 & **97.3** \\  & Average & 90.8 & 91.6 & 93.4 & **94.16** \\ \hline \multicolumn{2}{c}{Average} & 91.4 & 92.1 & 93.9 & **94.66** \\ \hline \end{tabular}
\end{table} 
Table 3: PRO scores of various methods on MVTec dataset. In Figure 4, failure cases are shown. From our visualization results, we have selected a few samples where errors occurred. First, the background may affect the results of our model predictions in the object class. In the first and second columns of Figure 4, it can see that there is a little contamination in the background, marked with red colored boxes, and the visualization results show that there is a high level of outliers in these areas. Second, the area of the anomalous region is not fully localized. In the third column of Figure 4, it can see that the display of the anomalous area is incomplete and only a part of it is shown. In Figure 5, we did the statistics of the abnormal scores. Normal samples are shown in blue and abnormal samples are shown in yellow. As can be seen from Figure 5, the model is able to distinguish the normal samples from the abnormal samples well.