Unscented Autoencoder

• URL: http://arxiv.org/abs/2306.05256v1
• Date: Thu, 8 Jun 2023 14:53:02 GMT
• Title: Unscented Autoencoder
• Authors: Faris Janjo\v{s}, Lars Rosenbaum, Maxim Dolgov, J. Marius Z\"ollner
• Abstract summary: Variational Autoencoder (VAE) is a seminal approach in deep generative modeling with latent variables. We apply the Unscented Transform (UT) -- a well-known distribution approximation used in the Unscented Kalman Filter (UKF) from the field of filtering. 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.
• Score: 3.0108936184913295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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