# Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder
## 3 Width-Adaptive Autoencoder
### Adding and Pruning Nodes

Table 10: Computational complexity and runtime comparison of Width-Adaptive AE and state-of-the-art approaches on the CIFAR-10 dataset. Table 11 presents a performance comparison of several models on the [PERSON]'s dataset, evaluating key metrics such as accuracy, F1-score, training loss, testing loss, and model width. Among all models, the WAAE demonstrates the strongest performance across all metrics, achieving the highest accuracy (87.04%) and F1-score (86.45%), along with the lowest training (0.7551) and testing loss (0.6925). This indicates that the model is not only highly accurate but also generalizes well, maintaining low error rates across both seen and unseen data. In contrast, while DEVDAN shows moderate results with an accuracy of 81.58% and F1-score of 81%, it still falls short of the WAAE. Its training and test losses are considerably higher (1.2428 and 1.1740), suggesting less efficient learning and potential overfitting or underfitting. SAQN, although having a wider architecture (width = 256), delivers slightly lower performance (76.31% accuracy, 77.86% F1-score), with marginally better losses compared to DEVDAN. Interestingly, SAQN with epochs performs even worse, indicating that simply increasing training epochs does not lead to better performance, possibly due to overfitting or a misaligned training strategy. The NDL model performs the worst across the board, with only 50.66% accuracy and 53.62% F1-score, despite having a wider architecture than DEVDAN. Its relatively low training and test losses do not translate into meaningful classification performance, suggesting that the model may struggle with learning discriminative features for this dataset. The WAAE's high width (2454), paired with low loss values and superior classification metrics, demonstrates the advantage of its dynamic architecture. By adaptively adjusting model width to the complexity of the data, it successfully captures informative representations, outperforming static-width models that rely on trial-and-error or fixed parameters. This reinforces the importance of architectural flexibility and data-driven design in achieving optimal performance on medical datasets like Parkinson's. * Computational complexity and time for Parkinson's

Table 12 highlights the computational complexity and runtime performance of the models on the Parkinson's dataset, emphasizing the trade-offs associated with the WAAE. While the WAAE records the highest training time (175.01 s) and training FLOPs (281.31 G), it maintains a relatively efficient test time (0.109 s), outperforming DEVDAN and closely matching SAQN variants. Its higher testing FLOPs (1.1248 G) and model FLOPs (0.0074 G) are a direct result of its significantly larger width, as also shown in Table 11. This relationship underscores the fact that the model's time and computational complexity scale with its width. However, this increase in complexity translates into substantial performance gains, confirming that the adaptive width mechanism enables the model to learn richer representations and achieve superior results on the [PERSON]'s dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **F1-Score** & **Train Loss** & **Test Loss** & **Width** \\ \hline DEVDAN & 81.58\% & 81\% & 1.2428 & 1.1740 & 104 \\ SAQN & 76.31\% & 77.86\% & 1.1120 & 1.0776 & 256 \\ SAQN with epochs & 68.42\% & 70.88\% & 1.0123 & 1.0717 & 256 \\ NDL & 50.66\% & 53.62\% & 1.0328 & 1.0169 & 200 \\ WAAE & 87.04\% & 86.45\% & 0.7551 & 0.6925 & 2454 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance measure comparison on [PERSON]’s dataset. * Learned width dynamics and performance metrics for Epilepsy

Table 13 presents a comparative performance analysis of different models on the Epilepsy dataset, clearly highlighting the effectiveness of the WAAE. Among all the models, WAAE achieves the highest accuracy (65.32%) and F1-score (65.21%), outperforming all competing approaches by a substantial margin.