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Second-order Tweedie from Surrogate Loss
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Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion
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Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion Second-order Tweedie from Surrogate Loss Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion Litu Rout 1,2   Yujia Chen 2   Abhishek Kumar 3   Constantine Caramanis 1   Sanjay Shakkottai 1   Wen-Sheng Chu 2   1 University of Texas, Austin   2 Google Research   3 Google DeepMind IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 Paper arXiv Code BibTex Abstract Sampling from the posterior distribution poses a major computational challenge in solving inverse problems using latent diffusion models. Common methods rely on Tweedie's first-order moments, which are known to induce a quality-limiting bias . Existing second-order approximations are impractical due to prohibitive computational costs, making standard reverse diffusion processes intractable for ...
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