  While enormous progress has been made to Variational Autoencoder (VAE) in
recent years, similar to other deep networks, VAE with deep networks suffers
from the problem of degeneration, which seriously weakens the correlation
between the input and the corresponding latent codes, deviating from the goal
of the representation learning. To investigate how degeneration affects VAE
from a theoretical perspective, we illustrate the information transmission in
VAE and analyze the intermediate layers of the encoders/decoders. Specifically,
we propose a Fisher Information measure for the layer-wise analysis. With such
measure, we demonstrate that information loss is ineluctable in feed-forward
networks and causes the degeneration in VAE. We show that skip connections in
VAE enable the preservation of information without changing the model
architecture. We call this class of VAE equipped with skip connections as SCVAE
and perform a range of experiments to show its advantages in information
preservation and degeneration mitigation.
