Variational Autoencoder

A generative model that extends the standard autoencoder by learning a probabilistic latent space. Rather than mapping each input to a single point, it maps inputs to a distribution, making it possible to sample new points from the latent space and decode them into new, realistic outputs. VAEs are used for image generation, data augmentation, and representation learning, and were an important precursor to modern diffusion models.