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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…
Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees
Azim Ospanov, Farzan Farnia
Evaluating the diversity of generative models without reference data poses methodological challenges. The reference-free Vendi and RKE scores address this by quantifying the 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…