AINeutralarXiv – CS AI · 7h ago6/10
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Initialization is Half the Battle: Generating Diverse Images from a Guidance Potential Posterior
Researchers have developed Diversity-inducing Initialization (DivIn), a method that addresses mode collapse in generative AI models by sampling initial noise from a guidance potential posterior rather than using standard Gaussian initialization. The technique uses Langevin dynamics to steer initial conditions toward diversity-rich regions while maintaining data validity, improving performance in both image and text-to-image generation tasks.