# Feature Transformation-Based Few-Shot Class-Incremental Learning
## 4 Simulation Experiments
### Baseline Experiments  
The classification results on the three datasets are reported in Figures 3-5, and Tables 1-3, respectively. Performance degradation is a metric proposed in [10], defined as the difference between the classification accuracy in the first session and that in the final session during the incremental learning process. Figure 4: Performance comparison between the proposed method and other approaches on the CIFAR100 Dataset. Figure 3: Performance comparison between the proposed method and other approaches on the miniImageNet Dataset. \begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Method** & \begin{tabular}{c} **Performance** \\ **Degradation** \\ \end{tabular} & **Method** & \begin{tabular}{c} **Performance** \\ **Degradation** \\ \end{tabular} \\ \hline iCaRL [4] & 44.13 & \
\begin{tabular}{c} FeTrIL [20] \\ PL-FSCIL \\ [21] \\ \end{tabular} & 26.13 \\ EEIL [3] & 41.82 &
\begin{tabular}{c} PL-FSCIL \\ [21] \\ \end{tabular} & 25.07 \\ NCM [5] & 47.21 & Cosine & **24.74** \\ TOPIC [9] & 36.89 & Ours & 24.85 \\ DeepEMD [25] & 26.41 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance degradation comparison on miniImageNet. Among the compared methods, iCaRL [4], EEIL [3], and NCM [5] are traditional class-incremental learning approaches, which have been adapted in this work for the few-shot class-incremental learning (FSCIL) setting. TOPIC [9] is a method specifically designed for FSCIL. All four of these methods adopt a non-fixed feature extractor strategy, meaning the feature extractor can be fine-tuned using the new class data during the incremental process. In contrast, DeepEMD [25], FeTrIL [20], PL-FSCIL [21], Cosine, and our proposed method adopt a fixed feature extractor strategy. That is, a feature extractor is trained first to project samples into a high-dimensional feature space, and all downstream classification methods operate on these extracted features. DeepEMD is a few-shot classification method that uses Earth Mover's Distance (EMD) for classification. Cosine performs classification directly based on cosine similarity. FeTrIL uses a fixed feature extractor and generates pseudo-features by translating new class features, enabling exemplar-free incremental learning with a simple linear classifier. PL-FSCIL builds on a pre-trained Vision Transformer with \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Performance** & \multirow{2}{*}{**Method**} & **Performance** \\  & **Degradation** & & **Degradation** \\ \hline iCaRL [4] & 50.45 & FeTrIL [20] & 25.02 \\ EEIL [3] & 48.33 & PL-FSCIL & 24.94 \\ NCM [5] & 50.71 & Cosine & 26.87 \\ TOPIC [9] & 34.92 & Ours & **24.45** \\ DeepEMD [25] & 25.40 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance degradation comparison on CIFAR100. Figure 5: Performance comparison between the proposed method and other approaches on the CUB Dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Performance** & \multirow{2}{*}{**Method**} & **Performance** \\  & **Degradation** & & **Degradation** \\ \hline iCaRL & 47.41 & FeTrIL & 26.37 \\ EEIL & 46.49 & PL-FSCIL & 25.33 \\ NCM & 48.72 & Cosine & 26.22 \\ TOPIC & 42.33 & Ours & **24.14** \\ DeepEMD & 27.70 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance degradation comparison on CUB. fixed features, adding Domain and FSCIL prompts to improve adaptation and incremental learning, while a prototype classifier preserves performance on old and new classes. Our proposed method achieves strong performance on both the miniImageNet and CUB datasets. It not only obtains the highest accuracy across all incremental sessions but also exhibits the lowest performance degradation. On miniImageNet and CUB, the performance results are decreased by 24.85 and 24.14 percentage points, respectively.