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摘要翻译：
对于成对MRFs和其他图形模型中的各种势函数，我们提出了一种精确的方法来大大加快信念传播(BP)。具体地说，我们的技术适用于大多数对状态的成对势被{\em截断}为常值的情况，就像在具有鲁棒势（如立体声）的MRF模型中通常做的那样，该模型对分配给不连续性的惩罚施加了一个上限；对于一个节点中的每一个$m$可能状态，相邻节点中只有少量$m$兼容状态被分配较轻的惩罚。与标准BP算法的计算复杂度为$O(mM)$相比，本文方法的计算复杂度为$O(M^2)$；与剪枝等相关技术相比，本文方法的计算复杂度是{em精确}；而且，该方法非常简单，易于实现。与以往一些加速BP算法的工作不同，我们的方法同时适用于和积和最大积BP算法，这使得它适用于任何需要边缘概率的应用，如最大似然估计。我们在一个立体MRF实例上演示了该技术，证实了该技术在不改变解的情况下加快了BP。
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英文标题：
《An Embarrassingly Simple Speed-Up of Belief Propagation with Robust
Potentials》
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作者：
James M. Coughlan, Huiying Shen
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最新提交年份：
2010
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
We present an exact method of greatly speeding up belief propagation (BP) for a wide variety of potential functions in pairwise MRFs and other graphical models. Specifically, our technique applies whenever the pairwise potentials have been {\em truncated} to a constant value for most pairs of states, as is commonly done in MRF models with robust potentials (such as stereo) that impose an upper bound on the penalty assigned to discontinuities; for each of the $M$ possible states in one node, only a smaller number $m$ of compatible states in a neighboring node are assigned milder penalties. The computational complexity of our method is $O(mM)$, compared with $O(M^2)$ for standard BP, and we emphasize that the method is {\em exact}, in contrast with related techniques such as pruning; moreover, the method is very simple and easy to implement. Unlike some previous work on speeding up BP, our method applies both to sum-product and max-product BP, which makes it useful in any applications where marginal probabilities are required, such as maximum likelihood estimation. We demonstrate the technique on a stereo MRF example, confirming that the technique speeds up BP without altering the solution.
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PDF链接：
https://arxiv.org/pdf/1010.0012
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