# O-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1903.01931 · HTML: https://mlsanity.com/paper/1903.01931/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network](https://mlsanity.com/paper/2307.15860/) (2023)
- [A novel adversarial learning strategy for medical image classification](https://mlsanity.com/paper/2206.11501/) (2022)
- [CKGAN: Training Generative Adversarial Networks Using Characteristic Kernel Integral Probability Metrics](https://mlsanity.com/paper/2504.05945/) (2025)

## Abstract (arXiv)

In this paper, we propose Orthogonal Generative Adversarial Networks (O-GANs). We decompose the network of discriminator orthogonally and add an extra loss into the objective of common GANs, which can enforce discriminator become an effective encoder. The same extra loss can be embedded into any kind of GANs and there is almost no increase in computation. Furthermore, we discuss the principle of our method, which is relative to the fully-exploiting of the remaining degrees of freedom of discriminator. As we know, our solution is the simplest approach to train a generative adversarial network with auto-encoding ability.