# Feature Transformation-Based Few-Shot Class-Incremental Learning
## 4 Simulation Experiments
### Ablation Experiments
#### 4.3.2 Effect of Different Few-Shot Tasks  
We train and fine-tune the feature transformation layer using few-shot tasks sampled from the base dataset. Although the task scale during incremental learning is determined by the dataset (e.g., 5-way 5-shot), the task scale used to train the transformation layer can be manually configured since it is based on the base classes. The scale of few-shot tasks can influence the final performance of the model. In this section, we evaluate several commonly used few-shot task configurations on the miniImageNet dataset. The results are presented in Table 4, where the best value for each metric is highlighted in bold. The results show that the number of classes (ways) in the few-shot tasks has a more significant impact on performance compared to the number of support samples (shots) per class. When the number of classes is set to 15, the overall performance is the best. Specifically, under the 15-way setting, the 1-shot configuration yields the best performance in the first session and on average, while the 10-shot configuration achieves the best performance in the final session and exhibits the lowest performance degradation. After balancing performance and efficiency, we select the 15-way 1-shot task configuration. Under this setting, the model achieves competitive performance with fewer training samples and faster convergence. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Number of classes in the & \multicolumn{3}{c}{5} & \multicolumn{3}{c}{10} \\ support set & & & & & \\ \hline Number of samples per class & 1 & 5 & 10 & 1 & 5 & 10 \\ \hline Session 0 & 70.27 & 71.03 & 71.16 & 70.95 & 71.26 & 71.01 \\ Session 8 & 45.40 & 46.22 & 46.75 & 45.77 & 47.00 & 46.84 \\ Avg. accuracy & 55.81 & 56.30 & 56.97 & 56.13 & 57.08 & 57.05 \\ Performance degradation & 24.87 & 24.81 & 24.41 & 25.18 & 24.26 & **24.17** \\ \hline Number of classes in the & \multicolumn{3}{c}{15} & \multicolumn{3}{c}{20} \\ support set & & & & & \\ \hline Number of samples per class & 1 & 5 & 10 & 1 & 5 & 10 \\ \hline Session 0 & **72.12** & 71.71 & 71.90 & 71.57 & 71.16 & 71.50 \\ Session 8 & 47.27 & 47.11 & **47.37** & 46.15 & 46.95 & 47.28 \\ Avg. accuracy & **57.63** & 57.57 & 57.58 & 56.85 & 57.10 & 57.42 \\ Performance degradation & 24.85 & 24.60 & 24.53 & 25.42 & 24.21 & 24.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of different few-shot task scales on model performance. Figure 6: Impact of mapping to different dimensions on model performance. An ideal set of class prototypes should satisfy the following property [14]: after centering and normalization, the inner product between any two distinct class means should equal \(-1/(K-1)\), where \(K\) is the number of classes. This theoretical value increases as the number of classes increases. In the figures, the blue curve represents the theoretical optimum as the number of classes grows over the incremental sessions. The red and purple curves show the expected inner product values before and after feature transformation, respectively. We observe that after applying feature transformation, the expected inner product values between class centers move significantly closer to the theoretical optimum. This indicates that the transformation improves the relative positioning of class prototypes in the feature space, making them more aligned with the ideal distribution. This improved alignment is consistent with the key properties of neural collapse, particularly the tendency of class means to form equiangular configurations. We further analyze the performance of base classes and newly introduced incremental classes throughout the learning process on miniImageNet as shown in Figure 10. The base class accuracy is calculated as the average accuracy over all base classes, while the incremental class accuracy is the average over all incremental classes learned up to the current session. The result shows that, regardless of whether feature transformation is applied, base classes consistently outperform incremental classes.