论文标题
NN模型可以清楚地学习平面布局吗？
Can an NN model plainly learn planar layouts?
论文作者
论文摘要
平面图纸往往在美学上令人愉悦。在此海报中，我们探讨了神经网络学习各种平面图类的能力。此外，我们还研究了该模型在概括平面性超出平面性方面的有效性。我们发现该模型可以胜过某些图形类别的常规技术。但是，该模型似乎更容易受到数据的随机性，并且似乎比预期的要鲁棒。
Planar graph drawings tend to be aesthetically pleasing. In this poster we explore a Neural Network's capability of learning various planar graph classes. Additionally, we also investigate the effectiveness of the model in generalizing beyond planarity. We find that the model can outperform conventional techniques for certain graph classes. The model, however, appears to be more susceptible to randomness in the data, and seems to be less robust than expected.
