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Variational autoencoder is a powerful deep generative model with variationalinference. The practice of modeling latent variables in the VAE's originalformulation as normal distributions with a diagonal covariance matrix limitsthe flexibility to match the true posterior distribution. We propose a newtransformation, dyadic transformation (DT), that can model a multivariatenormal distribution. DT is a single-stage transformation with low computationalrequirements. We demonstrate empirically on MNIST dataset that DT enhances theposterior flexibility and attains competitive results compared to other VAEenhancements.

Enhanced Variational Inference with Dyadic Transformation