How can Variational Autoencoders $($VAEs$)$ be used in anomaly detection?

The natural language processing of VAEs help them compute complex information. With their large scale transformer architecture, they can quickly process language$-$based information.

VAEs are trained with large datasets and they have the capability to future predict anomalies by analyzing the behaviors of a production systems.

VAEs can be trained on a dataset of normal data, and later on be used to identify instances that deviate from the normal data.