To resolve latent space drift when applying VAEs for anomaly detection in sequential data, you can follow the following :
- Introduce Temporal Consistency: Add a temporal regularization term to ensure the latent space representation remains stable over time.
- Use a Recurrent VAE (RVAE): Incorporate recurrent layers (e.g., LSTM or GRU) into the encoder and decoder to capture temporal dependencies.
- Latent Space Regularization: Apply a smoothness penalty to the latent space to prevent drastic changes across time steps.
- Cold Start for Anomalies: Use early stopping based on anomaly detection thresholds to prevent drift when training on sequential data.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Recurrent Layers: Using LSTM or GRU layers helps capture temporal dependencies, reducing latent space drift.
- Latent Space Regularization: Apply KL divergence and smoothness penalties to keep the latent space stable across time.
- Anomaly Detection: Use reconstructed data (or latent variables) to identify anomalies based on deviation from expected patterns.
Hence, these techniques can help mitigate latent space drift and stabilize the latent representations for sequential anomaly detection tasks.
