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Existing deep learning methods for graph matching (GM) problems usually considered affinity learning to assist combinatorial optimization in a feedforward pipeline, and parameter learning is executed by backpropagating the gradients of the matching loss. Such a pipeline pays little attention to the possible complementary benefit from the optimization layer to the learning component. In this paper, we overcome the above limitation under a deep bidirectional learning framework. Our methoddblp:conf/aaai/ZhaoTX21 fatcat:vul5g524kzd3legy7u5pbrnk3q