  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.
