# GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2101.09978 · HTML: https://mlsanity.com/paper/2101.09978/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 69 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows](https://mlsanity.com/paper/1906.12320/) (2019)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Revisiting Classifier Two-Sample Tests](https://mlsanity.com/paper/1610.06545/) (2016)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [pix2code: Generating Code from a Graphical User Interface Screenshot](https://mlsanity.com/paper/1705.07962/) (2017)
- [Semantic Object Accuracy for Generative Text-to-Image Synthesis](https://mlsanity.com/paper/1910.13321/) (2019)
- [An empirical study on evaluation metrics of generative adversarial networks](https://mlsanity.com/paper/1806.07755/) (2018)
- [Attribute-conditioned Layout GAN for Automatic Graphic Design](https://mlsanity.com/paper/2009.05284/) (2020)
- [Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps](https://mlsanity.com/paper/1802.02312/) (2018)
- [Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?](https://mlsanity.com/paper/2008.05132/) (2020)
- [Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning](https://mlsanity.com/paper/2003.00380/) (2020)
- [Owl Eyes: Spotting UI Display Issues via Visual Understanding](https://mlsanity.com/paper/2009.01417/) (2020)
- [Controllable and Progressive Image Extrapolation](https://mlsanity.com/paper/1912.11711/) (2019)
- [StoryDroid: Automated Generation of Storyboard for Android Apps](https://mlsanity.com/paper/1902.00476/) (2019)
- [Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce](https://mlsanity.com/paper/1908.10139/) (2019)
- [Colors -Messengers of Concepts: Visual Design Mining for Learning Color Semantics](https://mlsanity.com/paper/1505.06532/) (2015)
- [Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation](https://mlsanity.com/paper/1811.04172/) (2018)
- [Guigle: A GUI Search Engine for Android Apps](https://mlsanity.com/paper/1901.00891/) (2019)

## Built on by (most cited first, 17 of 17)

- [Understanding Mobile GUI: from Pixel-Words to Screen-Sentences](https://mlsanity.com/paper/2105.11941/) (2021)
- [MP-GUI: Modality Perception with MLLMs for GUI Understanding](https://mlsanity.com/paper/2503.14021/) (2025)
- [Boosting GUI Prototyping with Diffusion Models](https://mlsanity.com/paper/2306.06233/) (2023)
- [VisRefiner: Learning from Visual Differences for Screenshot-to-Code Generation](https://mlsanity.com/paper/2602.05998/) (2026)
- [Generative Colorization of Structured Mobile Web Pages](https://mlsanity.com/paper/2212.11541/) (2022)
- [Layout-Corrector: Alleviating Layout Sticking Phenomenon in Discrete Diffusion Model](https://mlsanity.com/paper/2409.16689/) (2024)
- [Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques](https://mlsanity.com/paper/2204.07641/) (2022)
- [GUI-GenBench: Evaluating Image Generation Models as Interactive GUI Environments](https://mlsanity.com/paper/2602.09007/) (2026)
- [CANVAS: A Benchmark for Vision-Language Models on Tool-Based User Interface Design](https://mlsanity.com/paper/2511.20737/) (2025)
- [GreaseVision: Rewriting the Rules of the Interface](https://mlsanity.com/paper/2204.03731/) (2022)
- [ULDGNN: A Fragmented UI Layer Detector Based on Graph Neural Networks](https://mlsanity.com/paper/2208.06658/) (2022)
- [GUILGET: GUI Layout GEneration with Transformer](https://mlsanity.com/paper/2304.09012/) (2023)
- [GUI Element Detection Using SOTA YOLO Deep Learning Models](https://mlsanity.com/paper/2408.03507/) (2024)
- [CoGen: Creation of Reusable UI Components in Figma via Textual Commands](https://mlsanity.com/paper/2601.10536/) (2026)
- [LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing](https://mlsanity.com/paper/2204.03994/) (2022)
- [UI Layers Merger: Merging UI layers via Visual Learning and Boundary Prior](https://mlsanity.com/paper/2206.13389/) (2022)
- [Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports](https://mlsanity.com/paper/2407.08610/) (2024)

## Abstract (arXiv)

Graphical User Interface (GUI) is ubiquitous in almost all modern desktop software, mobile applications, and online websites. A good GUI design is crucial to the success of the software in the market, but designing a good GUI which requires much innovation and creativity is difficult even to well-trained designers. Besides, the requirement of the rapid development of GUI design also aggravates designers' working load. So, the availability of various automated generated GUIs can help enhance the design personalization and specialization as they can cater to the taste of different designers. To assist designers, we develop a model GUIGAN to automatically generate GUI designs. Different from conventional image generation models based on image pixels, our GUIGAN is to reuse GUI components collected from existing mobile app GUIs for composing a new design that is similar to natural-language generation. Our GUIGAN is based on SeqGAN by modeling the GUI component style compatibility and GUI structure. The evaluation demonstrates that our model significantly outperforms the best of the baseline methods by 30.77% in Frechet Inception distance (FID) and 12.35% in 1-Nearest Neighbor Accuracy (1-NNA). Through a pilot user study, we provide initial evidence of the usefulness of our approach for generating acceptable brand new GUI designs.