  Variational Autoencoders (VAEs) are known to suffer from learning
uninformative latent representation of the input due to issues such as
approximated posterior collapse, or entanglement of the latent space. We impose
an explicit constraint on the Kullback-Leibler (KL) divergence term inside the
VAE objective function. While the explicit constraint naturally avoids
posterior collapse, we use it to further understand the significance of the KL
term in controlling the information transmitted through the VAE channel. Within
this framework, we explore different properties of the estimated posterior
distribution, and highlight the trade-off between the amount of information
encoded in a latent code during training, and the generative capacity of the
model.
