论文标题
GRCNN：用于从流程图合成程序的图形识别卷积神经网络
GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts
论文作者
论文摘要
程序合成是基于用户规范自动生成程序的任务。在本文中，我们提出了一个框架，该框架从流程图中综合了程序，这些程序是准确和直观的规格。为此，我们提出了一个称为GRCNN的深神经网络，该网络从其图像中识别出图结构。 GRCNN是经过训练的端到端，可以同时预测流程图的边缘和节点信息。实验表明，合成程序的准确率为66.4％，识别边缘和节点的精度分别为94.1％和67.9％。平均而言，合成一个程序大约需要60毫秒。
Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specifications. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and nodes are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.
