Use Anomaly Detection in Data Sets with VAE
Anomaly detection is a crucial task in various domains, including finance, cybersecurity, industrial quality control, and healthcare. It involves identifying data points or events that deviate significantly from the expected or normal behavior within a dataset (for example, finding a blue tennis ball inside a box of green tennis balls).
Detecting anomalies is essential for maintaining the integrity and security of systems, preventing fraud, and ensuring the quality of products and services. Generative AI models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), have gained popularity in recent years for their effectiveness in addressing these challenges.
In generative AI, anomaly detection is the process of identifying rare, unusual, or abnormal instances within a dataset. These instances can represent anything from fraudulent transactions in financial data to defective products in manufacturing processes. Let’s look at this from a VAE point of view.
The process of anomaly detection using a VAE is as follows:
- Generate a synthetic dataset with anomalies. This entails creating a first normal data set and a second data set that contains anomalous values (outliers).
- Define the VAE, which learns to reconstruct normal data. The VAE is then trained using mean squared error loss.
- Calculate the reconstruction errors for the test set. Reconstruction error ...
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