  Variational autoencoders trained to minimize the reconstruction error are
sensitive to the posterior collapse problem, that is the proposal posterior
distribution is always equal to the prior. We propose a novel regularization
method based on fraternal dropout to prevent posterior collapse. We evaluate
our approach using several metrics and observe improvements in all the tested
configurations.
