论文标题
通过组合求解器的黑盒分化的深度图匹配
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
论文作者
论文摘要
在组合优化和深度学习的交集的最新进展为基础上，我们提出了一个端到端的可训练架构，用于深度图匹配，其中包含未修改的组合求解器。利用经过重视的组合求解器的存在以及架构设计的一些改进，我们可以在深度图匹配基准测试基准方面进行最新的关键点对应关系。此外，我们强调了将求解器纳入深度学习体系结构的概念上的优势，例如有可能使用强大的多画匹配求解器进行后处理或对训练环境变化的漠不关心。最后，我们提出了两个新的具有挑战性的实验设置。该代码可在https://github.com/martius-lab/blackbox-deep-graph-matching上找到
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight the conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups. The code is available at https://github.com/martius-lab/blackbox-deep-graph-matching
