  Variational Auto-encoders (VAEs) have been very successful as methods for
forming compressed latent representations of complex, often high-dimensional,
data. In this paper, we derive an alternative variational lower bound from the
one common in VAEs, which aims to minimize aggregate information loss. Using
our lower bound as the objective function for an auto-encoder enables us to
place a prior on the bulk statistics, corresponding to an aggregate posterior
for the entire dataset, as opposed to a single sample posterior as in the
original VAE. This alternative form of prior constraint allows individual
posteriors more flexibility to preserve necessary information for good
reconstruction quality. We further derive an analytic approximation to our
lower bound, leading to an efficient learning algorithm - WiSE-ALE. Through
various examples, we demonstrate that WiSE-ALE can reach excellent
reconstruction quality in comparison to other state-of-the-art VAE models,
while still retaining the ability to learn a smooth, compact representation.
