| KEYWORD |
Generative Methods to Enhance Creativity in User Interface Design
keywords GENERATIVE ADVERSARIAL NETWORKS, HUMAN-COMPUTER INTERACTION, MACHINE LEARNING, USER EXPERIENCE, USER INTERFACE
Reference persons LUIGI DE RUSSIS
External reference persons Tommaso Calò
Research Groups DAUIN - GR-10 - Intelligent and Interactive Systems - e-LITE
Thesis type EXPERIMENTAL, RESEARCH
Description This thesis focuses on the use of generative methods to enhance creativity in User Interface (UI) design. The emergence of generative methods, particularly deep learning techniques, has provided new opportunities for computer-aided design. Recent developments in generative adversarial networks (GANs) and autoencoders have shown promising results in generating creative content in various domains such as graphics, music, and text. However, the application of these methods in the field of UI design remains largely unexplored.
UI design is a critical aspect of software development that greatly impacts user experience. However, the design process can be challenging due to the necessity of considering multiple factors, such as aesthetic appeal, usability, and accessibility. Generative methods have the potential to provide designers with novel and innovative design options, enhancing their creativity and supporting them in overcoming design challenges.
The main goals of this thesis are:
* Review the current state of the art in generative methods and their applications in creative design.
* Develop a generative model for UI design that can provide creative and usable design options.
* Evaluate the effectiveness and usability of the generated designs through user studies.
If appropriate, the outcome of the work will be released as an open-source project.
See also generative methods to enhance creativity in user interface design.pdf
Required skills Good programming skills and Python knowledge are required.
Deadline 31/01/2024
PROPONI LA TUA CANDIDATURA