## 4 Experiments and Analysis
### Analysis
#### 4.3.3 Impact of Classifiers  
To assess the impact of classifiers on continual learning methods, we conducted ablation experiments comparing the Softmax classifier and the C-NCM classifier. As shown in Figure 5a-c, the Softmax classifier suffers from severe catastrophic forgetting in the absence of replay, whereas the C-NCM classifier achieves performance nearly comparable to that of the Softmax classifier with replay, even without relying on replay. This demonstrates that  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PTM**} & \multicolumn{2}{c}{**ImageNet-R-T10**} & \multicolumn{2}{c}{**CUB-200-T10**} \\ \cline{2-5}  & **FAA** & **CAA** & **FAA** & **CAA** \\ \hline iBOT & 71.33 & 73.62 & 75.9 & 78.44 \\ DINO & 68.11 & 71.70 & 75.47 & 78.68 \\ MoCo & 63.77 & 68.26 & 61.52 & 67.51 \\ CLIP & **83.11** & **84.72** & **76.37** & **81.11** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall performance of continual learning with different pretrained paradigm. Bold values indicate the best-performing metrics in each comparison.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**CIFAR100-T10**} & \multicolumn{2}{c}{**ImageNet-R-T10**} \\ \cline{2-5}  & **FFM** & **F1-Score** & **FFM** & **F1-Score** \\ \hline Continual-CLIP & 6.78 & 0.43 & 5.89 & 0.49 \\ iCaRL & 5.66 & 0.52 & 3.32 & 0.61 \\ LwF-VR & 5.23 & 0.56 & 3.12 & 0.65 \\ ZSCL & 4.28 & 0.63 & 2.37 & 0.71 \\ IT-Prompt & **3.16** & **0.84** & **1.58** & **0.79** \\ \hline \hline \end{tabular}
\end{table}
Table 3: FFM and F1-score results in CIFAR100-T10 and ImageNet-R-T10 datasets. Bold values indicate the best-performing metrics in each comparison.  
the prototype-based nature of C-NCM enables more stable decision boundaries, reducing the dependency on replay buffers for knowledge retention.  
To further analyze the advantages of the C-NCM classifier over the Softmax classifier, we conducted a comparative study under identical hardware conditions using an NVIDIA GeForce RTX 4090 GPU. Specifically, we measured the training time per task required for each method to achieve optimal performance across different task sequences. As illustrated in Figure 5d-f, the Softmax classifier with replay incurs significantly higher training costs, as it must continuously revisit past samples to mitigate forgetting. In contrast, the C-NCM classifier achieves comparable or superior performance without replay, significantly reducing training overhead. As the task sequence expands, the Softmax classifier's reliance on replay leads to a continual increase in training time, whereas the C-NCM classifier remains computationally efficient, since only the classifier parameters related to the current task need updating, eliminating the need for revisiting past data.  
These results highlight the dual benefits of the C-NCM classifier: it not only mitigates catastrophic forgetting but also substantially improves training efficiency by reducing computational overhead. By eliminating the need for replay while maintaining competitive performance, C-NCM provides a more scalable and resource-efficient solution for continual learning. These findings further validate its effectiveness in real-world scenarios, where memory constraints and training efficiency are critical considerations.