## 4 Training Method Design
### Stages
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{\
\begin{tabular}{c} **Threshold for** \\ **Perturbation** \\ **Level** \\ \end{tabular} } & \multicolumn{2}{c}{**MED (Rec\_Steps)**} & \multicolumn{2}{c}{**MED (Rec\_Steps)**} & \multicolumn{2}{c}{**IRQ (Rec\_Steps)**} \\  & & \multicolumn{2}{c}{**
\begin{tabular}{c} **L\({}_{0}\)-** \\ **Attack** \\ \end{tabular} } & \multicolumn{2}{c}{**L\({}_{0}\)-** & \multicolumn{2}{c}{**L\({}_{0}\)-** & \multicolumn{2}{c}{**L\({}_{0}\)-** & \multicolumn{2}{c}{**L\({}_{0}\)-** & \multicolumn{2}{c}{**L\({}_{0}\)-** \\ **Attack** & **Attack** & **Attack** & **Attack** & **Attack** & **Attack** & **Attack** & **Attack** \\ \hline CIFAR-10 & 1 & 12 & 18 & 10 & 13 & 1 & 2 & 2 & 2 \\ CIFAR-10 & 2 & 21 & 29 & 20 & 21 & 1 & 1 & 3 & 3 \\ CIFAR-10 & 3 & 32 & 45 & 31 & 35 & 2 & 3 & 2 & 5 \\ CIFAR-10 & 4 & 39 & 50 & 39 & 42 & 3 & 3 & 4 & 4 \\ CIFAR-10 & 5 & 50 & 68 & 41 & 44 & 2 & 6 & 3 & 7 \\ CIFAR-10 & 6 & 91 & 111 & 59 & 52 & 4 & 5 & 5 & 6 \\ CIFAR-100 & 1 & 34 & 37 & 20 & 22 & 3 & 3 & 4 & 4 \\ CIFAR-100 & 2 & 39 & 41 & 42 & 42 & 5 & 3 & 3 & 4 \\ CIFAR-100 & 3 & 45 & 45 & 44 & 45 & 4 & 5 & 5 & 5 \\ CIFAR-100 & 4 & 46 & 49 & 49 & 50 & 2 & 4 & 4 & 4 \\ CIFAR-100 & 5 & 68 & 71 & 70 & 70 & 6 & 5 & 5 & 6 \\ CIFAR-100 & 6 & 100 & 99 & 80 & 85 & 7 & 6 & 5 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A comparison of the required number of performance recovery steps. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{ \begin{tabular}{c} **Threshold for** \\ **Perturbation** \\ **Level** \\ \end{tabular}  } & \multicolumn{2}{c}{**MED (Acc)**} & \multicolumn{2}{c}{**MED (Rec)**} & \multicolumn{2}{c}{**MED (Prec)**} & \multicolumn{2}{c}{**MED (Prec)**} \\  & & \multicolumn{2}{c}{**on Perturbed**} & \multicolumn{2}{c}{**for Superclass**} & \multicolumn{2}{c}{**on Perturbed**} & \multicolumn{2}{c}{**Level on**} \\  & & \multicolumn{2}{c}{**Test Data**} & \multicolumn{2}{c}{**Perturbed Test Data**} & \multicolumn{2}{c}{**Perturbed Test Data**} \\  & & \multicolumn{2}{c}{**L\({}_{0}\)-** & \multicolumn{2}{c}{**L\({}_{0}\)-** & \multicolumn{2}{c}{**L\({}_{0}\)-** & \multicolumn{2}{c}{**L\({}_{0}\)-** & \multicolumn{2}{c}{**L\({}_{0}\)-** & \multicolumn{2}{c}{**L\({}_{0}\)-** \\ **Attack** & **Attack** & **Attack** & **Attack** & **Attack** & **Attack** & **Attack** & **Attack** \\ \hline CIFAR-10 & 0 & 0.981 & 0.981 & 0.995 & 0.995 & 0.981 & 0.981 & 0.995 & 0.995 \\ CIFAR-10 & 1 & 0.975 & 0.967 & 0.980 & 0.970 & 0.979 & 0.978 & 0  
to the training mini-batch, which will then be included in the queue of the last labeled data. The threshold for detecting the real concept drift is set to 50 samples of one class in a container of another class. Table 5 shows the worst results of the adaptation speed of the lower hierarchical level of the classifier among the results for all concept drift combinations. Table 5 shows that performance recovery after the appearance of new classes occurs quickly, requiring up to 56 steps (tuning mini-batches) with the CIFAR-10 dataset, and up to 62 steps with the CIFAR-100 dataset. However, it took up to 95 steps with the CIFAR-10 dataset and 97 steps with the CIFAR-100 dataset to achieve performance recovery after the real concept drift. The IRQ of table values is less than 8. In other words, it takes more than 32 times less steps (mini-batches) for performance recovery than it does to learn from scratch. The behavior of the algorithm on both datasets is similar, but the CIFAR-100 case requires slightly more iterations for training and recovering.