Detecting the Unexpected: Generative Models for Anomaly Detection
Explore the use of GANs and VAEs in anomaly detection with a visually immersive flow diagram connecting data science concepts.
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Prompt
Generative Models for Anomaly Detection What Are Generative Models? Algorithms that learn the underlying data distribution to generate new, realistic data samples. Examples include GANs and VAEs, which are powerful tools for modeling complex data distributions. Key Generative Models: GANs: Two networks: a generator creates synthetic data, and a discriminator differentiates between real and fake data. For anomaly detection, anomalies are flagged when the discriminator finds it difficult to classify generated data as "normal." VAEs: Encode data into a compressed latent space and then reconstruct it. Anomalies are detected by measuring reconstruction errors (high errors suggest deviations from normal patterns).
