论文标题
随机估计量优于确定性的原因：鲁棒性，一致性和感知质量
Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality
论文作者
论文摘要
随机恢复算法允许探索与降解输入相对应的溶液空间。在本文中，我们揭示了随机方法比确定性方法的其他基本优势，这进一步激发了它们的使用。首先，我们证明任何具有完美感知质量并且其输出与输入一致的恢复算法都必须是后验采样器，因此必须是随机的。其次，我们说明，尽管确定性的恢复算法可能具有很高的感知质量，但只有使用极其敏感的映射填充所有可能的源图像的空间才能实现，这使它们非常容易受到对抗性攻击的影响。确实，我们表明，强制执行确定性模型对这种攻击的鲁棒性极大地阻碍了他们的感知质量，同时稳健的随机模型几乎不会影响其感知质量，并提高了其输出变异性。这些发现为促进随机恢复方法的进步提供了动力，为更好的恢复算法铺平了道路。
Stochastic restoration algorithms allow to explore the space of solutions that correspond to the degraded input. In this paper we reveal additional fundamental advantages of stochastic methods over deterministic ones, which further motivate their use. First, we prove that any restoration algorithm that attains perfect perceptual quality and whose outputs are consistent with the input must be a posterior sampler, and is thus required to be stochastic. Second, we illustrate that while deterministic restoration algorithms may attain high perceptual quality, this can be achieved only by filling up the space of all possible source images using an extremely sensitive mapping, which makes them highly vulnerable to adversarial attacks. Indeed, we show that enforcing deterministic models to be robust to such attacks profoundly hinders their perceptual quality, while robustifying stochastic models hardly influences their perceptual quality, and improves their output variability. These findings provide a motivation to foster progress in stochastic restoration methods, paving the way to better recovery algorithms.
