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

Supervisory Committee Chair

Timothy Andersen, Ph.D.

Supervisory Committee Member

Casey Kennington, Ph.D.

Supervisory Committee Member

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

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