We propose a surrogate function for efficient use of score-based priors for
Bayesian inverse imaging. Recent work turned score-based diffusion models into
probabilistic priors for solving ill-posed imaging problems by appealing to an
ODE-based log-probability function. However, evaluating this function is
computationally inefficient and inhibits posterior estimation of
high-dimensional images. Our proposed surrogate prior is based on the evidence
lower-bound of a score-based diffusion model. We demonstrate the surrogate
prior on variational inference for efficient approximate posterior sampling of
large images. Compared to the exact prior in previous work, our surrogate prior
accelerates optimization of the variational image distribution by at least two
orders of magnitude. We also find that our principled approach achieves
higher-fidelity images than non-Bayesian baselines that involve
hyperparameter-tuning at inference. Our work establishes a practical path
forward for using score-based diffusion models as general-purpose priors for
imaging.

我们提出了一种替代函数，用于有效地利用基于分数的先验对贝叶斯逆向成像进行建模。我们的替代先验在变分推断中对大型图像进行了高效的近似后验采样，并且相比先前方法的精确先验，至少将变分图像分布优化的速度加快了两个数量级。我们还发现，我们的方法在推理中实现了比涉及超参数调整的非贝叶斯基线更高保真度的图像。我们的研究为将基于分数的扩散模型作为成像的通用先验提供了实用的途径。

基于替代得分的高效贝叶斯计算成像

Efficient Bayesian Computational Imaging with a Surrogate Score-Based  Prior

Adversarial attacks have the potential to mislead deep neural network
classifiers by introducing slight perturbations. Developing algorithms that can
mitigate the effects of these attacks is crucial for ensuring the safe use of
artificial intelligence. Recent studies have suggested that score-based
diffusion models are effective in adversarial defenses. However, existing
diffusion-based defenses rely on the sequential simulation of the reversed
stochastic differential equations of diffusion models, which are
computationally inefficient and yield suboptimal results. In this paper, we
introduce a novel adversarial defense scheme named ScoreOpt, which optimizes
adversarial samples at test-time, towards original clean data in the direction
guided by score-based priors. We conduct comprehensive experiments on multiple
datasets, including CIFAR10, CIFAR100 and ImageNet. Our experimental results
demonstrate that our approach outperforms existing adversarial defenses in
terms of both robustness performance and inference speed.

我们引入了一个名为 ScoreOpt 的新的对抗防御方案，通过在测试时优化对抗样本，向原始清数据在得分优先的方向