  As one of the most popular generative models, Variational Autoencoder (VAE)
approximates the posterior of latent variables based on amortized variational
inference. However, when the decoder network is sufficiently expressive, VAE
may lead to posterior collapse; that is, uninformative latent representations
may be learned. To this end, in this paper, we propose an alternative model,
DU-VAE, for learning a more Diverse and less Uncertain latent space, and thus
the representation can be learned in a meaningful and compact manner.
Specifically, we first theoretically demonstrate that it will result in better
latent space with high diversity and low uncertainty awareness by controlling
the distribution of posterior's parameters across the whole data accordingly.
Then, without the introduction of new loss terms or modifying training
strategies, we propose to exploit Dropout on the variances and
Batch-Normalization on the means simultaneously to regularize their
distributions implicitly. Furthermore, to evaluate the generalization effect,
we also exploit DU-VAE for inverse autoregressive flow based-VAE (VAE-IAF)
empirically. Finally, extensive experiments on three benchmark datasets clearly
show that our approach can outperform state-of-the-art baselines on both
likelihood estimation and underlying classification tasks.
