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Scendi Score: Prompt-Aware Diversity Evaluation via Schur Complement of CLIP Embeddings
Azim Ospanov, Mohammad Jalali, Farzan Farnia
The use of CLIP embeddings to assess the fidelity of samples produced by text-to-image generative models has been extensively explored in the literature. While the widely adopted C…
Conditional Vendi Score: Prompt-Aware Diversity Evaluation for Generative AI Models and LLMs
Mohammad Jalali, Azim Ospanov, Amin Gohari +1
Generative models guided by text prompts are widely evaluated for fidelity and prompt alignment, yet their ability to produce diverse outputs remains underexplored. Existing divers…
Towards a Scalable Reference-Free Evaluation of Generative Models
Azim Ospanov, Jingwei Zhang, Mohammad Jalali +3
While standard evaluation scores for generative models are mostly reference-based, a reference-dependent assessment of generative models could be generally difficult due to the una…
Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach
Jingwei Zhang, Mohammad Jalali, Cheuk Ting Li +1
A fine-grained comparison of generative models requires the identification of sample types generated differently by each of the involved models. While quantitative scores have been…