A Variational Autoencoder (VAE) is a type of neural network that compresses input data into a smaller, structured latent space while learning to reconstruct the original data. Unlike a regular autoencoder, it assumes the latent space follows a probability distribution, allowing it to generate new, similar data samples. This makes VAEs useful for tasks like anomaly detection.