Ladder Variational Autoencoders

Understand

Variational Autoencoders are powerful models for unsupervised learning.

• However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models.
• We propose a new inference model, the Ladder Variational Autoencoder, that recursively corrects the generative distribution by a data dependent approximate likelihood in a process resembling the recently proposed Ladder Network.
• We show that this model provides state of the art predictive log-likelihood and tighter log-likelihood lower bound compared to the purely bottom-up inference in layered Variational Autoencoders and other generative models.