BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

Understand

With the introduction of the variational autoencoder (VAE), probabilistic latent variable models have received renewed attention as powerful generative models.

• However, their performance in terms of test likelihood and quality of generated samples has been surpassed by autoregressive models without stochastic units.
• Furthermore, flow-based models have recently been shown to be an attractive alternative that scales well to high-dimensional data.
• In this paper we close the performance gap by constructing VAE models that can effectively utilize a deep hierarchy of stochastic variables and model complex covariance structures.