Variational Autoencoders (VAEs) are generative models that are used to learn and generate new data samples, typically in the form of images, but they can be applied to other types of data as well. It consists of an encoder and a decoder. The encoder compresses the input data into a lower-dimensional representation, called the latent space or code. The decoder then reconstructs the original input data from the compressed representation.
