# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 4 Experiments and Discussions
### Experimental Results
#### 4.4.3 Ablation Experiments  
The method proposed in this study combines knowledge distillation loss, cross-entropy loss, class separation loss, and bias correction to deal with catastrophic forgetting,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Average Incremental Accuracy \(\pm\) Standard Deviations**} \\  & **2 Phases** & **5 Phases** & **10 Phases** \\ \hline iCaRL-CNN & 92.68 \(\pm\) 8.48 & 83.13 \(\pm\) 8.98 & 84.55 \(\pm\) 7.61 \\ iCaRL-NME & 93.97 \(\pm\) 6.65 & 86.95 \(\pm\) 7.14 & 86.51 \(\pm\) 6.54 \\ United-CNN & 94.54 \(\pm\) 5.96 & 92.71 \(\pm\) 3.82 & 89.10 \(\pm\) 5.91 \\ United-NME & 95.17 \(\pm\) 5.07 & 94.23 \(\pm\) 2.88 & 90.83 \(\pm\) 4.08 \\ CBesIL & 93.10 \(\pm\) 5.71 & 93.92 \(\pm\) 4.26 & 94.21 \(\pm\) 3.53 \\ Our-method & 98.04 \(\pm\) 2.06 & 97.73 \(\pm\) 1.44 & 97.17 \(\pm\) 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average incremental accuracy for 0 ur-method vs. state of the art (average incremental accuracy \(\pm\) standard deviations).  
Figure 5: Comparison of non-incremental and incremental learning time consumption.  
old and new class confusion, class imbalance. To analyze the impact of different components on incremental learning performance, each of the following three combinations of methods are compared.  
* Hybrid 1: training the model using cross-entropy loss (\(L_{CE}\)) and knowledge distillation loss (\(L_{KD}\)).
* Hybrid 2: training the model using cross-entropy loss (\(L_{CE}\)), knowledge distillation loss (\(L_{KD}\)), and class separation loss (\(L_{SP}\)).
* Our-method: the model is trained using cross-entropy loss (\(L_{CE}\)), knowledge distillation loss (\(L_{KD}\)), class separation loss (\(L_{SP}\)), and bias correction (Bic correction) methods.  
The experiments are conducted under a 5-phases incremental process, with 2 new classes incremented each time. Table 3 reports the average incremental accuracy and standard deviation with different components. As shown in Figure 6 and Table 3, Hybrid 1 uses knowledge distillation loss to preserve the knowledge of the old classes, the confusion between the old and new classes gradually increased with the incremental phases, and the model performance degradation is significant. Hybrid 2 is better than Hybrid 1 because the class separation loss focusing on class boundaries increases the distance between samples of different classes and reduces the distance within samples of the same class, which solves the classification confusion problem well. However, due to class imbalance between old and new classes, the recognition accuracy further decreases after multiple phases of incremental training. When the bias correction method is added to the training, the classification preference of the fully connected layer for the new class is corrected. The model's performance improves significantly and shows a slow forgetting during incremental learning. These results suggest that the bias problem caused by a class imbalance in incremental learning with exemplars significantly affects the incremental learning performance. Our proposed method is able to further improve the model recognition ability and achieve a perfect stability-plasticity compromise: it maintains the model's ability to discriminate between old tasks and learn new tasks well.