  The variational autoencoder (VAE) typically employs a standard normal prior
as a regularizer for the probabilistic latent encoder. However, the Gaussian
tail often decays too quickly to effectively accommodate the encoded points,
failing to preserve crucial structures hidden in the data. In this paper, we
explore the use of heavy-tailed models to combat over-regularization. Drawing
upon insights from information geometry, we propose $t^3$VAE, a modified VAE
framework that incorporates Student's t-distributions for the prior, encoder,
and decoder. This results in a joint model distribution of a power form which
we argue can better fit real-world datasets. We derive a new objective by
reformulating the evidence lower bound as joint optimization of KL divergence
between two statistical manifolds and replacing with $\gamma$-power divergence,
a natural alternative for power families. $t^3$VAE demonstrates superior
generation of low-density regions when trained on heavy-tailed synthetic data.
Furthermore, we show that $t^3$VAE significantly outperforms other models on
CelebA and imbalanced CIFAR-100 datasets.
