# GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint

arXiv: https://arxiv.org/abs/1811.07296 · HTML: https://mlsanity.com/paper/1811.07296/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [A survey on GANs for computer vision: Recent research, analysis and taxonomy](https://mlsanity.com/paper/2203.11242/) (2022)
- [Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2108.00274/) (2021)
- [Human Machine Co-Creation. A Complementary Cognitive Approach to Creative Character Design Process Using GANs](https://mlsanity.com/paper/2311.13960/) (2023)
- [Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning](https://mlsanity.com/paper/2504.17709/) (2025)
- [Generative multi-domain transfer learning for fault detection in data-scarce wind turbines](https://mlsanity.com/paper/2608.30323/) (2026)

## Abstract (arXiv)

We know SGAN may have a risk of gradient vanishing. A significant improvement is WGAN, with the help of 1-Lipschitz constraint on discriminator to prevent from gradient vanishing. Is there any GAN having no gradient vanishing and no 1-Lipschitz constraint on discriminator? We do find one, called GAN-QP. To construct a new framework of Generative Adversarial Network (GAN) usually includes three steps: 1. choose a probability divergence; 2. convert it into a dual form; 3. play a min-max game. In this articles, we demonstrate that the first step is not necessary. We can analyse the property of divergence and even construct new divergence in dual space directly. As a reward, we obtain a simpler alternative of WGAN: GAN-QP. We demonstrate that GAN-QP have a better performance than WGAN in theory and practice.