# KG-GAN: Knowledge-Guided Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1905.12261 · HTML: https://mlsanity.com/paper/1905.12261/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2019.

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

- [A survey on knowledge-enhanced multimodal learning](https://mlsanity.com/paper/2211.12328/) (2022)
- [Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation](https://mlsanity.com/paper/2101.00990/) (2021)
- [Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions](https://mlsanity.com/paper/2212.13591/) (2022)

## Abstract (arXiv)

Can generative adversarial networks (GANs) generate roses of various colors given only roses of red petals as input? The answer is negative, since GANs' discriminator would reject all roses of unseen petal colors. In this study, we propose knowledge-guided GAN (KG-GAN) to fuse domain knowledge with the GAN framework. KG-GAN trains two generators; one learns from data whereas the other learns from knowledge with a constraint function. Experimental results demonstrate the effectiveness of KG-GAN in generating unseen flower categories from seen categories given textual descriptions of the unseen ones.