VAE with a VampPrior

Understand

Many different methods to train deep generative models have been introduced in the past.

• In this paper, we propose to extend the variational auto-encoder (VAE) framework with a new type of prior which we call "Variational Mixture of Posteriors" prior, or VampPrior for short.
• The VampPrior consists of a mixture distribution (e.g., a mixture of Gaussians) with components given by variational posteriors conditioned on learnable pseudo-inputs.
• We further extend this prior to a two layer hierarchical model and show that this architecture with a coupled prior and posterior, learns significantly better models.