# 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 c c c} \hline \hline \multicolumn{1}{c}{**Category**} & \multicolumn{1}{c}{**AnGAN**} & \multicolumn{1}{c}{**RIAD**} & \multicolumn{1}{c}{**US**} & \multicolumn{1}{c}{**Pewd**} & \multicolumn{1}{c}{**PaDiM**} & \multicolumn{1}{c}{**Captuate**} & \multicolumn{1}{c}{**NSA [7]**} & \multicolumn{1}{c}{**Draem**} & \multicolumn{1}{c}{**CFLOW-AD [49]**} & \multicolumn{1}{c}{**UniAD**} & \multicolumn{1}{c}{**ADRD**} \\  & **[14]** & **[**43**]** & **[**14**]** & **[**44**]** & **[**13**]** & **[**61**]** & **[**13**]** & **[**61**]** & **[**18**]** & **[**10**]** & **[**13**]** & **[**10**]** & **[**13**]** & **[**10**]** \\ \hline \multirow{4}{*}{Texture} & Carpet & 54 & 84 & 92.16 & 92.9 & 96.8 & 93.9 & 95.6 & 97 & 98.73 & 99.6 & 98.5 & **100** & **99.8** \\  & Grad & 58 & 99.6 & 81 & 94.6 & 96.7 & **100** & 99.9 & 99.9 & 99.6 & 96.5 & **100** & **99.8** \\  & Leather & 64 & 100 & 88.2 & 90.9 & **100** & **100** & **99.9 & **100** & **100** & **100** & **100** & **100** \\  & Tile & 50 & 98.7 & 99.1 & 97.8 & 96.1 & 94.6 & 100 & 99.8 & 99.8 & 99.3 & 99 & **99.6** \\  & Wood & 62 & 93 & 97.2 & 96.5 & 99.2 & 99.1 & 97.5 & 99.1 & 99.1 & 99.2 & 99.8 & **99.8** \\  & Average & 57.6 & 55.1 & 91.5 & 94.5 & 98.8 & 97.5 & 98.6 & 91.2 & 94.7 & 99.6 & 99.8 & **99.8** \\ \hline \multirow{4}{*}{Object} & Bottie & 86 & 99.9 & 99.0 & 98.6 & 99.9 & 98.2 & 97.7 & 99.2 & 100 & **100** & **100** & **99.8** \\  & Calobe & 78 & 81.9 & 86.2 & 90.3 & 92.7 & 81.2 & 94.5 & 91.8 & 97.5 & 97.6 & 95 & **97.6** \\  & Capsule & 84 & 88.4 & 86.1 & 76.7 & 91.3 & 98.2 & 95.2 & 98.5 & 97.6 & 88.3 & 96.3 & 98.3 \\  & Hazultnut & 87 & 83.3 & 93.1 & 92 & 92.2 & 98.3 & 94.7 & 100 & 99.8 & 99.9 & 99.9 & **100** \\  & Pill & 87 & 88.5 & 82.0 & 94 & 98.7 & 99.9 & 98.7 & 98.7 & 99.2 & 99 & **100** & **100** \\  & Pill & 87 & 83.8 & 87.9 & 86.1 & 93.3 & 94.9 & 99.2 & 98.9 & 96.2 & 88.3 & 96.6 & 97.4 \\  & Score & 80 & 84.5 & 54.9 & 81.3 & 85.8 & 88.7 & 90.2 & 93.3 & 91.8 & 91.9 & 97 & **98.7** \\  & Tothbrush & 90 & 100 & 95.3 & 100 & 96.1 & 99.4 & 100 & 100 & 96.5 & 95 & **100** \\  & Tpmister & 80 & 90.9 & 81.8 & 91.5 & 97.4 & 96.1 & 95.1 & 93.1 & 95.2 & 100 & 96.2 & 98.1 \\  & Zipper & 78 & 98.1 & 91.9 & 97.9 & 90.3 & 99.8 & 93.0 & 98.8 & 90.7 & 98.5 & 97.6 \\  & Average & 82.6 & 89.9 & 85.8 & 90.8 & 93.8 & 95.5 & 96.5 & 97.41 & 97.6 & 95.7 & 97.6 & **99.18** \\ \hline \multicolumn{1}{c}{Average} & 74.27 & 91.7 & 87.7 & 92.1 & 95.5 & 96.1 & 97.2 & 98.27 & 99.27 & 99.6 & 98.72 & **99.43** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Image-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
**BTAD** & **AE + MSE**[47] & **AE + MSE + SSIM**[47] & **VT-ADL**[48] & **Ours** \\ \hline
0 & 0.49/- & 0.53/- & 0.99/- & 1.00/0.99 \\
1 & 0.92/- & 0.96/- & 0.94/- & 0.95/0.96 \\
2 & 0.95/- & 0.89/- & 0.77/- & 0.87/0.97 \\ Mean & 0.78/- & 0.79/- & 0.9/- & 0.94/0.98 \\ \hline \end{tabular}
\end{table} 
Table 4: Image-level and Pixel-level anomaly detection results on the BTAD dataset. Methods achieved for the top one ROC-AUC (%) are highlighted in bold. “-” means that no pixel-level anomaly score is given in the method. (Note: added reference numbers).