Question 10 of 10

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

Select an answer:

A. The natural language processing of VAEs helps them compute complex information. With their large-scale transformer architecture, they can quickly process language-based information.
B. VAEs can be trained on a dataset of normal data, and later be used to identify instances that deviate from the normal data.
C. VAEs are trained with large datasets, and they have the capability to predict future anomalies by analyzing the behaviors of production systems.

Variational Autoencoders (VAEs) can be used in anomaly detection by being trained on a dataset of normal data and then identifying instances that deviate from this normal data. They work by learning the essential features of the input data during the training phase and reconstructing the input using these features during the generation phase. This process allows them to detect anomalies based on how well the input data can be reconstructed.

Learn more about Variational Autoencoders (VAEs) in anomaly detection here:

https://brainly.com/question/51124030