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arXiv · 1901.10621

Enhanced Variational Inference with Dyadic Transformation

Sarin Chandy·Amin Rasekh
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Abstract

Variational autoencoder is a powerful deep generative model with variational inference. The practice of modeling latent variables in the VAE's original formulation as normal distributions with a diagonal covariance matrix limits the flexibility to match the true posterior distribution. We propose a new transformation, dyadic transformation (DT), that can model a multivariate normal distribution. DT is a single-stage transformation with low computational requirements. We demonstrate empirically on MNIST dataset that DT enhances the posterior flexibility and attains competitive results compared to other VAE enhancements.

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BibTeXRIS

Sarin Chandy, Amin Rasekh. 2019-03-08. Enhanced Variational Inference with Dyadic Transformation. https://arxiv.org/abs/1901.10621

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