Variational autoencoders (VAEs) are another type of generative model that aims to learn a low-dimensional representation of the original data in an unsupervised manner. VAEs consist of an encoder network that maps the input data to latent variables, and a decoder network that reconstructs the input data from the latent space. By sampling from the learned latent space, VAEs can generate new data points.