# Ensemble-Based Knowledge Distillation for Video Anomaly Detection
## 4 Experimental Evaluation
### Results of CBWC Approach  
The results of experiments with the CBWC approach are presented in the Tables 9 and 10. The normal results and results with reversed source and target datasets show that the student model again exhibits a similar behavioral pattern compared to EWC and CBMS approaches with respect to increasing alpha parameters. Additionally, the general performance of the student model is again slightly improved on specific alpha parameters compared to Tables 5 and 6 with respect to the EWC method, but no drastic improvement is observed compared to the CBMS method (Tables 7 and 8). We explain a possible reason for this in Section 4.7. Compared to the CBMS, instead of selecting a feedback distillation signal from one of the GCN or AR-Net teacher models in CBMS, the CBWC approach provides the AR-Net student model with the weighted combined signal feedback synthesized from both the GCN and AR-Net model teacher models and helps to alleviate catastrophic forgetting by improving the performance on the test set of the source dataset.  
In Tables 11 and 12, two confusion matrices for individual GCN teacher models are shown. These matrices are obtained by following a similar procedure as in Section 4.2. An individual GCN Teacher model is firstly trained on a training set of the Source Dataset (UCF-Crime) and then directly tested on a test set of the Source Dataset and Target Dataset (RWF-2000). Tables 11 and 12 results show consistent behavior with respective Table 2 results in which the teacher model's performance is better on the test set of Source Dataset compared to the test set of the Target Dataset.  
In Tables 13 and 14, again, two confusion matrices for individual AR-Net teacher models are presented. These matrices are extracted again by following a similar procedure as in Section 4.2. This time, the individual AR-Net Teacher model is again firstly trained on a training set of Source Dataset (UCF-Crime) and then directly tested on a test set of the Source Dataset and Target Dataset (RWF-2000). Tables 13 and 14 results also show consistent behavior with respect to Table 1 results, in which the teacher model has drastically better performance on the test set of Source Dataset compared to the test set of the Target Dataset.  
In Tables 15 and 16, two confusion matrices are calculated by training the AR-Net student model with the EWC approach (Table 5) with the best overall alpha parameter (0.1) and testing on a test set of UCF-Crime and RWF-2000 Dataset. Please notice, that in Table 16, the student model's performance is drastically improved on RWF-2000 test set (Target Dataset) compared to the Tables 12 and 14 and more importantly, in Table 15, the student model also shows comparable performance on the UCF-Crime test set (Source Dataset) compared to Tables 11 and 13 and shows consistent behavior with respect to Table 5.  
Finally in Table 17, different misclassification statistics are shown for both individual GCN and AR-Net teacher models. The first column represents the total misclassified sample count for the GCN teacher model with respect to Tables 11 and 12, while the second column represents the total misclassified sample count for the AR-Net teacher model with respect to Tables 13 and 14. More importantly, the third column (Common) represents the sample count both misclassified by GCN and AR-Net teacher models. With respect to this table, we concluded that very often, when the GCN teacher model mistakenly classifies a sample, the AR-Net teacher model also shows the same behavior. We think that this behavior explains, to a certain extent, the limited performance increase in our EWC and CBWC ensemble-based distillation formulations in this study compared to the CBMS method.