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A new measurement-manifold compatibility measure reveals how to improve generative model trustworthiness in medical imaging by guiding more effective sampling strategies.
Robustifying the prior update in diffusion models can drastically enhance the fidelity of image reconstructions, achieving over 91% preference in human evaluations.
Hallucinations in diffusion models aren't just mode interpolation gone wrong, but instabilities on the model's manifold, and squashing its local intrinsic dimension can fix them.
