The LSTM module in the IAE-LSTM-KL model can be replaced with other memory-augmented neural network architectures, such as Transformer-based models or Memory Networks.
Transformer-based Models: Transformers have shown success in capturing long-range dependencies in sequential data. By replacing the LSTM module with a Transformer architecture, the model can potentially improve its ability to capture temporal relationships and dependencies in the data. Transformers can handle parallel processing of sequences and may offer better performance in certain scenarios.
Memory Networks: Memory-augmented neural networks, such as Memory Networks, are designed to store and retrieve information from an external memory component. By incorporating Memory Networks in place of the LSTM module, the model can benefit from enhanced memory capacity and improved handling of long-term dependencies. Memory Networks can effectively store relevant information for anomaly detection tasks and retrieve it when needed.
The impact of replacing the LSTM module with these architectures would depend on the specific characteristics of the dataset and the complexity of the anomaly detection task. Experimentation and comparative analysis would be necessary to evaluate the performance gains or trade-offs of using alternative memory-augmented neural network architectures in the IAE-LSTM-KL model.