## 6 Discussion  
The proposed model of the classifier has a multi-section structure designed to implement adaptive calculations and increase the generalization capabilities of the model due to self-knowledge distillation. Accuracy under perturbation and a recovery speed should be compared for a model, which uses outputs of intermediate sections with a model using only last output (at the last model's layer) to identify the influence of the multi-section structure on the resilience of the model. Conceptually, the resilient classifier can be built from modules with different micro-architectures. The system's property of resilience should be nevertheless preserved owing to the proposed macro-architecture and training method. However, in the problem of classification analysis, along with the convolutional building blocks, the transformer building blocks have become widespread. Therefore, it is interesting to consider the behavior of the proposed classifier with a backbone based on the well-known blocks of the Swin transformer [55]. In addition, the conventional approach to building the classification head of the model is to use the dense layer and Softmax output normalization. Therefore, it is worth checking how the replacement of the prototype classifier with the dense layer will affect the classifier's resilience. Table 6 shows a comparison between the models, using the outputs of individual sections and the model which only uses a single output at the last layer in light of their accuracy under perturbation by taking into account the different ways of implementing the backbone and classifier head. Table 7 shows a comparison of recovery speed depending on the above modifications. For simplicity, only the CIFAR-10 dataset and two different types of perturbations are used--fault injection with a fault_rate = 0.3 and adversarial L\({}_{\infty}\) attack with threshold = 3. The IRQ of accuracy values from Table 6 does not exceed 0.03, and the IRQ of the number of recovery steps from Table 7 does not exceed 5. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{3}{*}{**Perturbation**} & \multicolumn{3}{c}{**Number of Steps for Training**} & \multicolumn{3}{c}{**Number of Steps for Supervised Recovery**} \\  & **from Scratch** & & **(Maximum Value among Experiments)** \\ \cline{2-5}  & **CIFAR-10** & **CIFAR-100** & **CIFAR-100** & **CIFAR-100** \\ \hline Add one new class & 2400 & 2800 & 33 & 38 \\ Add two new classes & 2400 & 3000 & 56 & 62 \\ Real concept drift between pair of classes & 2800 & 3000 & 73 & 75 \\ Real concept drift between three classes & 2800 & 3200 & 95 & 97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance recovery rates after adding new classes or mutual class drift at the lower hierarchical level. Tables 6 and 7 show that all the considered modifications are not superior to the proposed options in Figure 1. However, the accuracy of the classifier and the speed of performance recovery are noticeably higher if the intermediate outputs of the model are used. At the same time, the Swin transformer blocks provide slightly higher accuracy values compared to the ResNet blocks, especially under the influence of adversarial attacks. However, recovering the performance of the classifier with backbone based on Swin transformer blocks requires more tuning steps compared to the usage of ResNet blocks. Similarly, the effects of cutting off the outputs of intermediate sections on recovery speed after the influence of concept drifts should be considered. Table 8 shows the worst results of the adaptation speed of the lower hierarchical level of the classifier among the results for all perturbing combinations, in the case of the cut-off outputs of intermediate sections. Tables 5 and 8 show that using the outputs of all sections of the model increases the speed of performance recovery by more than 20%; as such, it requires 20% less labeled data to achieve the same result.