GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts
GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts
复制标题
GRCNN：用于从流程图合成程序的图形识别卷积神经网络
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Z. Yang
中科院分区：
文献类型：
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作者：
Lin Cheng;Z. Yang
程序合成是根据用户需求自动生成程序的任务。在本文中，我们提出了一个框架，从流程图中综合程序，作为准确和直观的规范。为了做到这一点，我们提出了一个称为GRCNN的深度神经网络，它可以从图像中识别图结构。GRCNN采用端到端训练，可以同时预测流程图的边缘和节点信息。实验表明，该方法合成程序的准确率为66.4%，识别边缘和节点的准确率分别为94.1%和67.9%。合成一个程序平均需要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.