# Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder
## 3 Width-Adaptive Autoencoder
### Adding and Pruning Nodes
This can become particularly demanding for large-scale or high-resolution datasets. Efficient approximation methods or layer-wise adaptation strategies could be explored to alleviate this burden. Moreover, the current method relies on empirically defined thresholds to guide width adaptation. These thresholds can significantly influence convergence behavior and model quality. A more principled or data-driven approach to adapt thresholds dynamically may improve robustness and reduce the need for manual tuning. In addition, the width adaptation mechanism is designed for offline training. Applying WAAE to streaming data or continual learning settings would require additional mechanisms to handle evolving data distributions and avoid catastrophic forgetting. Incorporating memory-efficient or incremental learning strategies could be a promising direction for extending WAAE's applicability. Finally, while autoencoders are traditionally evaluated using reconstruction error, extending WAAE to downstream tasks such as classification, clustering, or anomaly detection requires further investigation. The effect of adaptive latent width on task-specific performance metrics remains an open question. Future work will aim to address these limitations by integrating adaptive threshold learning, accelerating training through surrogate optimization, and testing WAAE in online learning environments and diverse application domains. Conceptualization, M.A., O.B. and M.M.B.I. ; methodology, M.A., O.B. and M.M.B.I. ; software, M.A. ; validation, M.A. and O.B. ; formal analysis, M.A. and O.B. ; investigation, M.A. and O.B. ; resources, M.A. and O.B. ; data curation, M.A. and O.B. ; writing--original draft preparation, M.A. ; writing--review and editing, M.A. and O.B. ; visualization, M.A. and O.B. ; supervision, O.B. and M.M.B.I. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. **Data Availability Statement**: Publicly available datasets were analyzed in this study. MNIST data can be found here: [[[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/))] (accessed on 20 April 2025); CIFAR-10 data can be found here: [[[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html))] (accessed on 20 April 2025); [PERSON]'s dataset can be found here: [[[https://archive.ics.uci.edu/dataset/470/parkinson+s+disease+classification](https://archive.ics.uci.edu/dataset/470/parkinson+s+disease+classification)]([https://archive.ics.uci.edu/dataset/470/parkinson+s+disease+classification](https://archive.ics.uci.edu/dataset/470/parkinson+s+disease+classification))] (accessed on 20 April 2025); Epilepsy dataset can be found here: [[[https://www.kaggle.com/datasets/harunshimanto/epileptic-seizure-recognition](https://www.kaggle.com/datasets/harunshimanto/epileptic-seizure-recognition)]([https://www.kaggle.com/datasets/harunshimanto/epileptic-seizure-recognition](https://www.kaggle.com/datasets/harunshimanto/epileptic-seizure-recognition))] (accessed on 20 April 2025). The authors are grateful for the support of the Research Center of the College of Computer and Information Sciences, King Saud University. The authors thank the Deanship of Scientific Research and RSSU at King Saud University for their technical support. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AE & Autoencoder \\ AI & Artificial Intelligence \\ ANN & Artificial Neural Network \\ CIFAR-10 & Canadian Institute for Advanced Research \\ CNN & Convolutional Neural Network \\ DAE & Denoising Auto-Encoder \\ DEVDAN & Deep evolving denoising Auto-Encoder \\ \end{tabular} 

## References

* (1) [PERSON]; [PERSON] _Deep Learning: Concepts and Architectures_; [PERSON], [PERSON], Eds.