Bayesian diffusion models memorize training data when mutual information between restricted observations and training data exceeds log dataset size, and generalize otherwise.
Denoising diffusion probabilistic models.Ad- vances in neural information processing systems, 33:6840–6851
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
Immune2V immunizes images against dual-stream I2V generation by enforcing temporally balanced latent divergence and aligning generative features to a precomputed collapse trajectory, yielding stronger persistent degradation than image-level baselines.
LiBaGS scores and selects synthetic data near decision boundaries using proximity, uncertainty, density, and validity, with boundary-gap allocation and marginal stopping to improve training accuracy.
3DTV proposes a feedforward network for real-time sparse-view interpolation using Delaunay triplet selection and a pose-aware coarse-to-fine depth module, outperforming real-time baselines without scene-specific optimization.
citing papers explorer
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An exact information theory of generalization phase transitions in Bayesian diffusion models
Bayesian diffusion models memorize training data when mutual information between restricted observations and training data exceeds log dataset size, and generalize otherwise.
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Immune2V: Image Immunization Against Dual-Stream Image-to-Video Generation
Immune2V immunizes images against dual-stream I2V generation by enforcing temporally balanced latent divergence and aligning generative features to a precomputed collapse trajectory, yielding stronger persistent degradation than image-level baselines.
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LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection
LiBaGS scores and selects synthetic data near decision boundaries using proximity, uncertainty, density, and validity, with boundary-gap allocation and marginal stopping to improve training accuracy.
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3DTV: A Feedforward Interpolation Network for Real-Time View Synthesis
3DTV proposes a feedforward network for real-time sparse-view interpolation using Delaunay triplet selection and a pose-aware coarse-to-fine depth module, outperforming real-time baselines without scene-specific optimization.