Geometry-Guided Self-Assessment of Generative AI Models: Enhancing Diversity, Fidelity, and Control

" data-medium-file="https://www.marktechpost.com/wp-content/uploads/2024/08/Screenshot-2024-08-21-at-4.53.10-PM-300x187.png" data-large-file="https://www.marktechpost.com/wp-content/uploads/2024/08/Screenshot-2024-08-21-at-4.53.10-PM-1024x637.png" tabindex="0" role="button"> " data-medium-file="https://www.marktechpost.com/wp-content/uploads/2024/08/Screenshot-2024-08-21-at-4.53.10-PM-300x187.png" data-large-file="https://www.marktechpost.com/wp-content/uploads/2024/08/Screenshot-2024-08-21-at-4.53.10-PM-1024x637.png" tabindex="0" role="button">Deep generative models learn continuous data representations from a limited set of training samples, with global metrics like Fréchet Inception Distance (FID) often used to evaluate their performance. However, these models may perform inconsistently across different regions of the learned manifold, especially in foundation models like Stable Diffusion, where generation quality can vary based on […]

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