Variational autoencoders (VAEs) are a type of generative Autoencoders.
They use a Bayesian latent encoding for the input dataset.
VAEs vs. GANs
VAEs have fallen out of fashion when GANs became popular, because they were able to get visually interesting results more easily. However, some works a few years later seem to show that they have similar potential (Vahdat and Kautz 2020).
- Vahdat, Arash, and Jan Kautz. July 8, 2020. "NVAE: A Deep Hierarchical Variational Autoencoder". arXiv:2007.03898 [Cs, Stat]. http://arxiv.org/abs/2007.03898.
- Bishop, Christopher M.. 1994. “Mixture Density Networks”. Aston University.