Variational Autoencoders (VAEs)

Variational Autoencoders (VAEs) are a type of generative model that learns the probability distribution of data in order to generate new samples with similar characteristics. Unlike GANs, which focus on realism, VAEs emphasize capturing meaningful latent representations of data.

VAEs are widely used in image synthesis, anomaly detection, and feature extraction. They are particularly useful in healthcare for medical image analysis, allowing AI to generate missing or enhanced medical scans for diagnosis.