Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood.  In particular, it is commonly believed that Gaussian encoder/decoder assumptions reduce the effectiveness of VAEs in generating realistic samples .  In this regard, we rigorously analyze the VAE objective, differentiating situations where this belief is and is not actually true .  We then leverage the corresponding insights to develop a simple VAE enhancement that requires no additional hyperparameters or sensitive tuning .  Quantitatively, this proposal produces crisp samples and stable FID scores that are actually competitive with a variety of GAN models, all while retaining desirable attributes of the original VAE architecture . The code for our model is available at \url{https://github.com/daib13/TwoStageVAE}.
We closely analyze the VAE objective function and draw novel conclusions that lead to simple enhancements. Proposes a two-stage VAE method to generate high-quality samples and avoid blurriness. This paper analyzes the Gaussian VAEs. The paper provides a number of theoretical results on "vanilla" Gaussian Variational Auto-Encoders, which are then used to build a new algorithm called "2 stage VAEs".