To convey neural network architectures in publications, appropriate visualizations are of great importance. While most current deep learning papers contain such visualizations, these are usually handcrafted just before publication, which results in a lack of a common visual grammar, significant time investment, errors, and ambiguities. Current automatic network visualization tools focus on debugging the network itself and are not ideal for generating publication visualizations. Therefore, we present an approach to automate this process by translating network architectures specified in Keras into visualizations that can directly be embedded into any publication. To do so, we propose a visual grammar for convolutional neural networks (CNNs), which has been derived from an analysis of such figures extracted from all ICCV and CVPR papers published between 2013 and 2019. The proposed grammar incorporates visual encoding, network layout, layer aggregation, and legend generation. We have further realized our approach in an online system available to the community, which we have evaluated through expert feedback, and a quantitative study. It not only reduces the time needed to generate network visualizations for publications, but also enables a unified and unambiguous visualization design.
翻译:为了在出版物中传达神经网络结构,适当的直观化非常重要。 虽然目前大部分深层学习论文包含这种直观化,但通常都是在出版前手工制作的,导致缺乏共同的视觉语法、大量时间投资、错误和模糊。当前的自动网络可视化工具侧重于调试网络本身,不适于生成出版物的可视化。因此,我们提出了一个自动化这一过程的方法,将Keras中指定的网络结构转化为可直接嵌入任何出版物的可视化。为了做到这一点,我们提议为共导神经网络绘制视觉语法(CNNs),这是根据对2013年至2019年出版的所有ICCV和CVPR论文中提取的此类数字的分析得出的。拟议的语法包括视觉编码、网络布局、层集和图象生成。我们通过专家反馈和定量研究进一步认识到了我们在社区可利用的在线系统中采用的方法,我们通过定量研究对它进行了评估。我们不仅缩短了生成出版物网络可视化所需的时间,而且还有助于统一和清晰的视觉设计。