Publication Date
5-2023
Date of Final Oral Examination (Defense)
3-10-2023
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Computer Science
Department
Computer Science
Major Advisor
Timothy Andersen, Ph.D.
Advisor
Casey Kennington, Ph.D.
Advisor
Edoardo Serra, Ph.D.
Abstract
This research investigates the effectiveness of a conditional image generator trained on a restricted number of unlabeled images for image-to-image translation in computer vision. While previous research has focused on using labeled data for image labeling in conditional image generation, this study proposes an original framework that utilizes self-supervised classification on generated images. The proposed approach, which combines Conditional GAN and Semantic Clustering, showed promising results. However, this study has several limitations, including a limited dataset and the need for significant computational power to generate a single UI design. Further research is needed to optimize the performance of the proposed approach for real-world applications.
DOI
https://doi.org/10.18122/td.2044.boisestate
Recommended Citation
Kiesecker, Hailee, "Exploring the Capability of a Self-Supervised Conditional Image Generator for Image-to-Image Translation without Labeled Data: A Case Study in Mobile User Interface Design" (2023). Boise State University Theses and Dissertations. 2044.
https://doi.org/10.18122/td.2044.boisestate
Files over 30MB may be slow to open. For best results, right-click and select "save as..."