# Variational Inference: A Unified Framework of Generative Models and Some Revelations

arXiv: https://arxiv.org/abs/1807.05936 · HTML: https://mlsanity.com/paper/1807.05936/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Amortized Variational Inference: A Systematic Review](https://mlsanity.com/paper/2209.10888/) (2022)
- [A Mathematical Framework for Learning Probability Distributions](https://mlsanity.com/paper/2212.11481/) (2022)
- [Diffusion-based Target Sampler for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2303.12724/) (2023)

## Abstract (arXiv)

We reinterpreting the variational inference in a new perspective. Via this way, we can easily prove that EM algorithm, VAE, GAN, AAE, ALI(BiGAN) are all special cases of variational inference. The proof also reveals the loss of standard GAN is incomplete and it explains why we need to train GAN cautiously. From that, we find out a regularization term to improve stability of GAN training.