VAE and Anomaly Detection
- Let's now move to talking about an application of generative AI that may not be as obvious as it's used in generating images, like we have seen earlier, audio or text. But it's still very important application nonetheless, and it is going to be the anomaly detection. One of the main models that we use in this space is Variational Autoencoders, referred as VAE. These models can be used for anomaly detection by training the model on a dataset of normal data, and then using the trained model to identify instances that deviate from the normal data. This can be used to detect anomalies in a wide range of situations, like finding fraud in financial transactions, spotting flaws in manufacturing or finding security breaches in a network. For example, Uber has used VAE for anomaly detection in their financial transactions to detect fraud. Another example would be Google has also used VAE to detect network intrusions using anomaly detection and another one of a real world application of VAE would be anomaly detection in industrial quality control. In this scenario, a VAE can be trained on a dataset of images of normal products and then used to identify images of products that deviate from the normal data. In this way, it can be used to detect defects in products such as scratches, dents, or misalignments. Another real world example would be healthcare where VAE is used to detect anomalies in medical imaging such as CT scans and MRI, like Children's National Hospital in Washington, DC uses a generative AI model to analyze electronic health records. The model uses data such as vital signs, laboratory results and demographic information to predict which patients are at risk of sepsis, allowing healthcare providers to intervene early and improve patient outcomes. Variational Autoencoders are a flexible, generative model that are not only able to detect anomalies but are also a part of the architecture of several other generative AI models.
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