Congratulations to Ganning Zhao for passing her defense. Ganning’s thesis title is “Learning to Generate Better: Visual Refinement and Evaluation in Generative AI Models.” Her Dissertation Committee included Jay Kuo (Chair), Antonio Ortega, and Stefanos Nikolaidis (Outside Member). The Committee members were pleased with the quality of Ganning’s thesis work. Thanks to our lab members for participating in her rehearsal and providing valuable feedback.
Here is the abstract of Ganning’s thesis:
Generative artificial intelligence (GenAI) has advanced rapidly in recent years, finding widespread applications in numerous domains. Generative models (GMs), which produce new data by learning underlying data distributions, typically require vast quantities of training examples—often in the millions or billions. Acquiring and labeling such large datasets is expensive and labor-intensive, prompting researchers to use synthetic data for training. However, the limited quality of these synthetic samples can restrict model performance. Furthermore, accurately evaluating the quality of generated images is critical for guiding improvements in generative models. This dissertation addresses two core challenges: (1) refining synthetic images, and (2) evaluating the quality of generated samples. Each challenge is tackled with a foundational approach and its subsequent enhancement.
