CDPA scales diffusion-based reconstruction to large 3D volumes by conditioning 2D models on initial 3D reconstructions plus data-consistency alignment, delivering state-of-the-art results on synthetic and real CBCT data.
Diffusion active learn- ing: Towards data-driven experimental design in computed tomography
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
GSAL combines diffusion-based visual difficulty scoring with hierarchical semantic coverage to improve active learning retrieval of subtle and rare visual anomalies over standard uncertainty and diversity methods.
UCD adjusts diffusion-based 3D molecular graph generation to handle epistemic uncertainty, improving sample quality and reaching new benchmark performance.
citing papers explorer
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Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
CDPA scales diffusion-based reconstruction to large 3D volumes by conditioning 2D models on initial 3D reconstructions plus data-consistency alignment, delivering state-of-the-art results on synthetic and real CBCT data.
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Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena
GSAL combines diffusion-based visual difficulty scoring with hierarchical semantic coverage to improve active learning retrieval of subtle and rare visual anomalies over standard uncertainty and diversity methods.
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Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation
UCD adjusts diffusion-based 3D molecular graph generation to handle epistemic uncertainty, improving sample quality and reaching new benchmark performance.