# Enhanced Variational Inference with Dyadic Transformation

## TL;DR

This paper introduces dyadic transformation, a new method to improve the flexibility of variational autoencoders by better modeling the posterior distribution, leading to improved performance on MNIST.

## Contribution

The paper proposes dyadic transformation, a computationally efficient single-stage transformation that enhances the posterior modeling capability of VAEs.

## Key findings

- DT improves posterior flexibility in VAEs
- Achieves competitive results on MNIST
- Low computational overhead

## 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.