While the KL divergence penalty approach used in the IAE-LSTM-KL model is effective in forcing the latent features to follow a Gaussian distribution and mitigating feature collapse in the SVDD module, there are potential limitations to consider:
Sensitivity to Hyperparameters: The effectiveness of the KL divergence penalty is dependent on the choice of hyperparameters, such as the weight assigned to the penalty term. Suboptimal hyperparameters may lead to subpar performance or instability in the model.
Mode Collapse: In some cases, the KL divergence penalty may inadvertently lead to mode collapse, where the model focuses on a limited set of latent features, ignoring the diversity in the data distribution. This can result in a loss of information and reduced anomaly detection performance.
Computational Complexity: Calculating the KL divergence for each sample in the dataset can be computationally intensive, especially for large datasets. This may impact the training time and overall efficiency of the model.
To address these limitations, techniques such as adaptive weighting of the KL divergence term based on the data distribution, regularization methods to prevent mode collapse, and optimization strategies to reduce computational complexity can be implemented. Additionally, exploring alternative divergence measures or regularization techniques may offer improvements in handling the limitations of the KL divergence penalty approach.