# Autoencoding Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2004.05472 · HTML: https://mlsanity.com/paper/2004.05472/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [AE-StyleGAN: Improved Training of Style-Based Auto-Encoders](https://mlsanity.com/paper/2110.08718/) (2021)
- [Pixel VQ-VAEs for Improved Pixel Art Representation](https://mlsanity.com/paper/2203.12130/) (2022)
- [IGAN: Inferent and Generative Adversarial Networks](https://mlsanity.com/paper/2109.13360/) (2021)
- [Deep Learning to Jointly Schema Match, Impute, and Transform Databases](https://mlsanity.com/paper/2207.03536/) (2022)
- [Flow Battery Manifold Design with Heterogeneous Inputs Through Generative Adversarial Neural Networks](https://mlsanity.com/paper/2508.08863/) (2025)

## Abstract (arXiv)

In the years since Goodfellow et al. introduced Generative Adversarial Networks (GANs), there has been an explosion in the breadth and quality of generative model applications. Despite this work, GANs still have a long way to go before they see mainstream adoption, owing largely to their infamous training instability. Here I propose the Autoencoding Generative Adversarial Network (AEGAN), a four-network model which learns a bijective mapping between a specified latent space and a given sample space by applying an adversarial loss and a reconstruction loss to both the generated images and the generated latent vectors. The AEGAN technique offers several improvements to typical GAN training, including training stabilization, mode-collapse prevention, and permitting the direct interpolation between real samples. The effectiveness of the technique is illustrated using an anime face dataset.