A Variational Autoencoder (VAE) is a type of autoencoder designed to learn efficient representations of data by modeling a distribution over the latent space. Unlike traditional autoencoders, VAEs introduce a probabilistic approach by imposing a prior distribution on the latent space and learning to approximate this distribution. This allows VAEs to generate new data samples by sampling from the learned distribution, making them suitable for generative tasks and anomaly detection. VAEs are widely used in applications where generating new, realistic data samples is crucial, such as in image generation and data augmentation.