CA-DPS approximates the covariance of the reverse diffusion process via a finite-difference Hessian estimate and uses it to improve posterior sampling for linear inverse problems.
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Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling
CA-DPS approximates the covariance of the reverse diffusion process via a finite-difference Hessian estimate and uses it to improve posterior sampling for linear inverse problems.