# Bidirectional Generative Modeling Using Adversarial Gradient Estimation

arXiv: https://arxiv.org/abs/2002.09161 · HTML: https://mlsanity.com/paper/2002.09161/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

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

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Distributional Principal Autoencoders](https://mlsanity.com/paper/2404.13649/) (2024)
- [Non-Asymptotic Error Bounds for Bidirectional GANs](https://mlsanity.com/paper/2110.12319/) (2021)

## Abstract (arXiv)

This paper considers the general f-divergence formulation of bidirectional generative modeling, which includes VAE and BiGAN as special cases. We present a new optimization method for this formulation, where the gradient is computed using an adversarially learned discriminator. In our framework, we show that different divergences induce similar algorithms in terms of gradient evaluation, except with different scaling. Therefore this paper gives a general recipe for a class of principled f-divergence based generative modeling methods. Theoretical justifications and extensive empirical studies are provided to demonstrate the advantage of our approach over existing methods.