5 papers
Imaginative Generative AI: Crossing the Entropy Wall into Worlds Beyond Imitation
Hossein Goli, Farzan Farnia, Amin Gohari
Generative AI models are primarily designed to imitate the data distribution, an objective that neither corrects diversity lost by a learned generator nor defines how generation sh…
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 outputs remains underexplored. Existing diversity metr…
Stable Source Coding
Zhenduo Wen, Amin Gohari
A source encoder is stable if a small change in the source sequence (e.g., changing a few symbols) results in a small (or bounded) change in the output codeword. By this definition…
SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Score
Mohammad Jalali, Haoyu Lei, Amin Gohari +1
Diffusion models have demonstrated remarkable success in high-fidelity image synthesis and prompt-guided generative modeling. However, ensuring adequate diversity in generated samp…
A New Upper Bound for Distributed Hypothesis Testing Using the Auxiliary Receiver Approach
Zhenduo Wen, Amin Gohari
This paper employs the add-and-subtract technique of the auxiliary receiver approach to establish a new upper bound for the distributed hypothesis testing problem. This new bound h…