#mode-collapse News & Analysis

3 articles tagged with #mode-collapse. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · Mar 127/10

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Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergences

Researchers introduce Gradient Flow Drifting, a new mathematical framework for generative AI models that connects the Drifting Model to Wasserstein gradient flows of KL divergence under kernel density estimation. The framework includes a mixed-divergence strategy to avoid mode collapse and extends to Riemannian manifolds for improved semantic space applications.

$KL

AINeutralarXiv – CS AI · Jun 26/10

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"I've Seen How This Goes": Characterizing Diversity via Progressive Conditional Surprise

Researchers propose a novel metric called 'Decan' for measuring diversity in AI-generated creative outputs using in-context learning and language model probabilities, achieving 84.6% accuracy on benchmark tests. The approach detects mode collapse and diversity loss across training stages without requiring specialized embedding models or human annotation, offering a practical tool for evaluating generative AI systems.

AINeutralarXiv – CS AI · Jun 26/10

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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.