TVCG · 2021
Net2Vis - A Visual Grammar for Automatically Generating Publication-Tailored CNN Architecture Visualizations
Alex Bäuerle, Christian van Onzenoodt, and Timo Ropinski
Abstract
To convey neural network architectures in publications, appropriate visualizations are of great importance. This project is aimed at automatically generating such visualizations from code. Thus, we are able to employ a common visual grammar, reduce the time investment towards these visualizations significantly, and reduce errors in these visualizations.