信念传播：学习BP层标记问题
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
摘要：
许多研究人员提出，将深度神经网络与图形模型相结合，可以创建更高效、更好的正则化复合模型。在实践中实现这一点的主要困难是与合适的学习目标的差异，以及与推理的近似的必要性。在这项工作中，我们采用最简单的推理方法之一，截断的最大乘积置信传播，并添加必要的内容，使其成为深度学习模型的适当组件：我们将其连接到边缘损失的学习公式，并计算反向传播操作。该BP层可以用作卷积神经网络（CNN）中的最终或中间块，允许我们设计一个由BP推理和不同尺度的CNN组成的分层模型。该模型适用于一系列密集的预测问题，是很好的训练，并提供立体，光流和语义分割的参数有效和强大的解决方案。
英文摘要：
It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main difficulties in implementing this in practice are associated with a discrepancy in suitable learning objectives as well as with the necessity of approximations for the inference. In this work we take one of the simplest inference methods, a truncated max-product Belief Propagation, and add what is necessary to make it a proper component of a deep learning model: We connect it to learning formulations with losses on marginals and compute the backprop operation. This BP-Layer can be used as the final or an intermediate block in convolutional neural networks (CNNs), allowing us to design a hierarchical model composing BP inference and CNNs at different scale levels. The model is applicable to a range of dense prediction problems, is well-trainable and provides parameter-efficient and robust solutions in stereo, optical flow and semantic segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
