Enhanced Variational Inference with Dyadic Transformation.
arXiv: Learning(2019)
Abstract
Variational autoencoder is a powerful deep generative model with variational inference. The practice of modeling latent variables in the VAEu0027s 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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Key words
dyadic transformation
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