# Reverse Knowledge Distillation with Two Teachers for Industrial Defect Detection
## 4 Experiments
### Results  
For comparisons with other work, we use the results mentioned in the original papers for compared methods. In image-level anomaly detection, we compare our model with the five kinds of existing models, including image restoration models, embedding-based models, abnormal sample synthesis models, flow-based models, and knowledge distillation models. The image restoration models contain AnoGAN [44], UniAD [45] and RIAD [46]. The embedding-based models consist of Psvdd [47] and PaDiM [35]. Abnormal samplesynthesis models contain Cutpaste [6], NSA [7] and Draem [38]. Models for knowledge distillation involve US [14] and ADRD [15]. CFLOW-AD [48] is based on flow models. Our baseline is based on the ADRD model. Table 1 shows the results of our image-level anomaly detection. From Table 1, it can see that our model outperformed other models in terms of the overall average value, achieving 99.43. In addition, from the perspective of texture and object class, the results of our method are also higher than other models, reaching 99.68 and 99.18, respectively. It shows that the knowledge distillation model is valuable in image anomaly detection. In addition, it's obvious that our model gains further improvement compared with the ADRD baseline and outperforms other models. It demonstrates that the two teacher networks, the attention mechanism, and the inconsistent teacher-student network module have a positive effect. The US is similar to our method, but it does not use the inverse teacher-student network architecture, we believe that the student network may not be good at identifying anomalous regions in the inverse structure. Some categories have better results than ours, such as Grid, Capsule, and Pill. However, our results are almost the same as theirs. For pixel-level anomaly detection, the comparison baselines include US [14], SPADE [32], PaDiM [35], Cutpaste [6], Draem [38], ADRD [15], NSA [7], CFLOW-AD [48] and UniAD [45]. Compared to the methods provided at the image-level anomaly detection, some models are not added to our table, and the data are not provided in the paper. From Table 2, it can see that our method outperforms the other methods in pixel-level anomaly detection with 97.87, which proves that our method is effective. In the texture class and target class, it can see that the result of our method is the best in the target class, reaching 98.27, but the texture class is not. The Draem method process segments the simulated anomaly samples after simulating the anomaly samples. With this approach, it may have good results for pixel-level discrimination. But our method is directly comparing the similarity between regional feature vectors. In Table 3, we show the PRO scores of various methods in the Mvtec dataset. As can be seen from the table, we achieved the best result of 94.66. We only compared three methods since other literature did not provide PRO scores. We also contrast our model with a few others on the BTAD dataset, such as the autoencoder [50] and the VT-ADL method [49]. From Table 4, it can see that the performance of our method outperforms other models in image-level anomaly detection. Since the other methods do not give pixel-level anomaly detection scores, it can not compare them. Individually, the two categories 1 and 2 are better than our results in the AE + MSE + SSIM method, which mainly uses the autoencoder method. The MSE is the mean square error. The SSIM is the luminance, structure, and contrast of the image.