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papersSEP 12 04:00 UTC

Information-Theoretic Framework Unifies Generalization Bounds for VAEs and Diffusion Models

A new arXiv paper derives generalization guarantees for both variational autoencoders and diffusion models within a single information-theoretic framework. The analysis exploits the encoder-generator structure shared by the two model families, which earlier theoretical work had largely treated separately. The authors report bounds that clarify how the shared architecture affects performance on unseen data.

arXiv Variational Autoencoders Generative models diffusion-models generalization bounds information theory

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGGeneralization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis ↗SEP 11 04:00 UTC

arXiv cs.AIGeneralization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis ↗SEP 12 04:00 UTC