Unscented Autoencoder
Faris Janjoš Lars Rosenbaum Maxim Dolgov J. Marius Zöllner
The Variational Autoencoder (VAE) is a seminal approach in deep generative
modeling with latent variables. Interpreting its reconstruction process as a
nonlinear transformation of samples from the latent posterior distribution, we
apply the Unscented Transform (UT) -- a well-known distribution approximation
used in the Unscented Kalman Filter (UKF) from the field of filtering. A finite
set of statistics called sigma points, sampled deterministically, provides a
more informative and lower-variance posterior representation than the
ubiquitous noise-scaling of the reparameterization trick, while ensuring
higher-quality reconstruction. We further boost the performance by replacing
the Kullback-Leibler (KL) divergence with the Wasserstein distribution metric
that allows for a sharper posterior. Inspired by the two components, we derive
a novel, deterministic-sampling flavor of the VAE, the Unscented Autoencoder
(UAE), trained purely with regularization-like terms on the per-sample
posterior. We empirically show competitive performance in Fr\'echet Inception
Distance (FID) scores over closely-related models, in addition to a lower
training variance than the VAE.