Generative Model Output Evaluation & Quality Control
ImageAssessed outputs from text-to-image and text-generation models across prompt fidelity, perceptual quality, compositional accuracy, and stylistic consistency. Used perceptual and statistical evaluation methods including FID, Inception Score, and CLIP-based similarity to benchmark generative model performance. Documented evaluation methodologies, scoring rubrics, and experimental findings in reproducible, clearly structured reports. • Critiqued image quality and semantic alignment against prompts using embedding similarity. • Created scoring rubrics to standardize evaluation and quality control. • Reported edge cases and measurable failure modes to inform model iteration. • Prepared evaluation artifacts that support ongoing ML research and development.