# GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network

arXiv: https://arxiv.org/abs/2203.03827 · HTML: https://mlsanity.com/paper/2203.03827/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2022.

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

- [Interrogating AI: Characterizing Emergent Playful Interactions with ChatGPT](https://mlsanity.com/paper/2401.08405/) (2024)
- [GUI-GenBench: Evaluating Image Generation Models as Interactive GUI Environments](https://mlsanity.com/paper/2602.09007/) (2026)
- [GUILGET: GUI Layout GEneration with Transformer](https://mlsanity.com/paper/2304.09012/) (2023)
- [Bridging Gulfs in UI Generation through Semantic Guidance](https://mlsanity.com/paper/2601.19171/) (2026)
- [Evaluation of Sketch-Based and Semantic-Based Modalities for Mockup Generation](https://mlsanity.com/paper/2303.12709/) (2023)
- [LikeThis! Empowering App Users to Submit UI Improvement Suggestions Instead of Complaints](https://mlsanity.com/paper/2603.04245/) (2026)

## Abstract (arXiv)

Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and techniques that support inspiration usually only focus on example browsing with limited user control or similarity-based example retrieval, leading to undesirable design outcomes such as focus drift and design fixation. To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. A quantitative evaluation revealed that the outputs of GANSpiration-based example suggestion approaches are relevant to the input design, and at the same time include diverse instances. A user study with professional UI/UX practitioners showed that the examples suggested by our approach serve as viable sources of inspiration for overall design concepts and specific design elements. Overall, our work paves the road of using advanced generative machine learning techniques in supporting the creative design practice.