## 4 Experiments and Results
### Incremental Recognition Performance
For example, the accuracies of iCaRL's Task_1 are 0.9834 in the three scenarios. This is because their training data are exactly the same. Furthermore, in our method, Task_3 of Scenario_A and Task_2 of Scenario_B have the same accuracy (they are both 0.9662). Similar situations include Task_4 of Scenario_A and Task_2 of Scenario_C (0.9565), Task_5 of Scenario_A, Task_3 of Scenario_B (0.9680), etc. This is because our method trains these dictionaries separately for each class of targets. The recognition accuracy is only related to the total number of classes, and it has nothing to do with the step size during incremental learning. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Scenario\_A**} & \multicolumn{8}{c}{**Scenario\_B**} & \multicolumn{8}{c}{**Scenario\_C**} \\ \cline{2-13}  & **Task\_1** & **Task\_2** & **Task\_3** & **Task\_4** & **Task\_5** & **Task\_6** & **Task\_7** & **Task\_1** & **Task\_2** & **Task\_3** & **Task\_4** & **Task\_1** & **Task\_2** & **Task\_3** \\ \hline Replay & 0.9770 & 0.9527 & 0.9414 & 0.9370 & 0.9345 & 0.9358 & 0.9592 & 0.9770 & 0.9534 & 0.9212 & 0.9456 & 0.9770 & 0.9713 & 0.9612 \\ iCaRL & 0.9834 & 0.9612 & 0.9414 & 0.9514 & 0.9457 & 0.9619 & 0.9604 & 0.9834 & 0.9692 & 0.9435 & 0.9834 & 0.9679 & 0.9715 \\ Wa & 0.9834 & 0.9669 & 0.9496 & 0.9383 & 0.9414 & 0.9507 & 0.9559 & 0.9834 & 0.9624 & 0.9505 & 0.9464 & 0.9834 & 0.9688 & 0.9666 \\ Podnet & 0.9859 & 0.9754 & 0.9564 & 0.9545 & 0.9558 & 0.9521 & 0.9633 & 0.9859 & 0.9609 & 0.9547 & 0.9386 & 0.9859 & 0.9657 & 0.9604 \\ ICAC & 0.9949 & 0.9804 & 0.9676 & 0.9565 & 0.9483 & 0.9342 & 0.9176 & 0.9949 & 0.9735 & 0.963 & 0.9477 & 0.9949 & 0.9713 & 0.9564 \\ ChessIL & 0.9806 & 0.828 & 0.7744 & 0.7633 & 0.7511 & 0.7313 & 0.7215 & 0.9806 & 0.8113 & 0.7639 & 0.7021 & 0.9806 & 0.7749 & 0.7175 \\ Our Method & 0.9987 & 0.9896 & 0.9662 & 0.9707 & 0.9680 & 0.9707 & 0.9736 & 0.9987 & 0.9662 & 0.9680 & 0.9736 & 0.9987 & 0.9707 & 0.9736 \\ Joint Training & 0.9962 & 0.9962 & 0.9917 & 0.9925 & 0.9968 & 0.9963 & 0.9951 & 0.9962 & 0.9917 & 0.9968 & 0.9951 & 0.9962 &Different from the overall accuracy in Table 3, we analyzed the recognition accuracy of various class targets in Task_7 (Scenario_A). The recognition results are presented in Table 4.  
Figure 4: Recognition accuracies of different methods under three scenarios. (**a**) Scenario_A; (**b**) Scenario_B; (**c**) Scenario_C. Six methods, including Joint Training, Replay, iCaRL, Wa, Podnet and our method, are compared in Table 4. Obviously, as a benchmark, Joint Training has the best performance on each task. Then, our method performs better on five classes of targets (BMP2, BTR70, T72, BTR60 and D7) compared with Replay, iCaRL, Wa and Podnet. Furthermore, we find that several incremental learning methods achieve significantly lower accuracy on BRDM2 than other classes. For example, the accuracies of Replay, iCaRL, Wa and our method on BRDM2 are 0.0942, 0.0480, 0.0617 and 0.0977 lower than their respective overall accuracies. Furthermore, we compare the time consumed by several methods, including Replay, iCaRL, Wa, Podnet, and our method. The time taken by them to complete the seven tasks in Scenario_A is presented in Table 5. Among them, Replay requires the least time, and it is 310 s. Following the Replay method, our method consumes 457 s. The time consumed by iCaRL, Wa and Podnet is 28 s, 67 s and 472 s more than our method, respectively. On the whole, there is little difference in the time consumed by iCaRL, Wa and our method. Our method has some advantages in the time of model training. These methods are deployed on the computer with Intel Core i7-8700K CPU, NVIDIA GTX 1080 Ti GPU and 16 GB memory.