[R][P] Trying to understand the generative properties of autoencoders

A while back, I came across the "[From Variational to Deterministic Autoencoders](https://arxiv.org/abs/1903.12436)", which provided a novel insight into the generative properties of autoencoders by framing the objective through the lens of regularization. However, I couldn't help but notice that the deterministic models studied felt incomplete, namely due to the inherent lack of sampling in those models (which is something that the authors acknowledge).

To provide a short recap of the paper, the authors surgically decompose the variational autoencoder objective into …

authors autoencoder case deviation distribution general leads machinelearning noise paper recap standard vae variance