Abstract
Designing and creating a Graphical User Interface (GUI) is a difficult and slow process. It requires a number of professions to all contribute to its development and it can be heavily detrimental to a product if implemented poorly. This research aims to investigate a method of using Generative Adversarial Networks (GANs) to generate new and usable designs for GUIs. GANs are a relatively new architecture for adversarial learning and have been used to good effect in replicating instances of a real dataset. The primary aim is to develop a GAN that is capable of processing a collection of existing GUIs and learn how to replicate these to allow for creation of further designs. These GUI designs need to be formatted in a manner that enables modification, allowing for them to be used by a development team to enhance their production process. Completed work demonstrates numerous approaches at using GANs to create text files that contain the component elements of a GUI. Their results and the release of a similar research paper (GUIGAN) has led to a new approach focusing on more abstract data representation, with a quality control system for ensuring the output data is properly formatted. It is hypothesised that the approach will develop a model capable of creating new, editable GUI designs.
Original language | English |
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Title of host publication | ICMI'21 |
Subtitle of host publication | proceedings 23rd ACM International Conference on Multimodal Interaction |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 802–806 |
Number of pages | 5 |
ISBN (Print) | 9781450384810 |
DOIs | |
Publication status | Published - 18 Oct 2021 |
Event | 23rd ACM International Conference on Multimodal Interaction - Le Westin Montréal, Montreal, Canada Duration: 18 Oct 2021 → 22 Oct 2021 https://icmi.acm.org/2021/index.php?id=home |
Conference
Conference | 23rd ACM International Conference on Multimodal Interaction |
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Abbreviated title | ICMI '21 |
Country/Territory | Canada |
City | Montreal |
Period | 18/10/21 → 22/10/21 |
Internet address |
Keywords
- Generative adversarial networks
- Graphical user interfaces,
- Machine learning
- Datasets