Variational Autoencoder

A Variational Autoencoder (VAE) is a type of machine learning model that learns to compress data into a simplified, probabilistic form and then reconstruct it back. Think of it like summarizing complex images or sounds into a compact representation that captures their essential features, while allowing some randomness. This process helps the model generate new, realistic data similar to the original inputs by sampling from the learned distribution. VAEs are used in tasks like generating images, enhancing data, and understanding complex patterns. They balance data compression with the ability to produce new, similar data points.