Haehn Daniel, Tompkin James, Pfister Hanspeter
IEEE Trans Vis Comput Graph. 2018 Aug 20. doi: 10.1109/TVCG.2018.2865138.
Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks? We investigate this question by reproducing Cleveland and McGill's seminal 1984 experiments, which measured human perception efficiency of different visual encodings and defined elementary perceptual tasks for visualization. We measure the graphical perceptual capabilities of four network architectures on five different visualization tasks and compare to existing and new human performance baselines. While under limited circumstances CNNs are able to meet or outperform human task performance, we find that CNNs are not currently a good model for human graphical perception. We present the results of these experiments to foster the understanding of how CNNs succeed and fail when applied to data visualizations.
卷积神经网络能够在图像上成功执行许多计算机视觉任务。对于可视化而言，当应用于图形感知任务时，卷积神经网络的表现如何？我们通过重现克利夫兰和麦吉尔1984年具有开创性的实验来研究这个问题，该实验测量了人类对不同视觉编码的感知效率，并为可视化定义了基本的感知任务。我们在五个不同的可视化任务上测量了四种网络架构的图形感知能力，并与现有的和新的人类性能基线进行比较。虽然在有限的情况下，卷积神经网络能够达到或超过人类的任务表现，但我们发现，卷积神经网络目前并不是人类图形感知的良好模型。我们展示这些实验的结果，以促进对卷积神经网络在应用于数据可视化时如何成功和失败的理解。
