  When optimizing convolutional neural networks (CNN) for a specific
image-based task, specialists commonly overshoot the number of convolutional
layers in their designs. By implication, these CNNs are unnecessarily resource
intensive to train and deploy, with diminishing beneficial effects on the
predictive performance.
  The features a convolutional layer can process are strictly limited by its
receptive field. By layer-wise analyzing the size of the receptive fields, we
can reliably predict sequences of layers that will not contribute qualitatively
to the test accuracy in the given CNN architecture. Based on this analysis, we
propose design strategies based on a so-called border layer. This layer allows
to identify unproductive convolutional layers and hence to resolve these
inefficiencies, optimize the explainability and the computational performance
of CNNs. Since neither the strategies nor the analysis requires training of the
actual model, these insights allow for a very efficient design process of CNN
architectures, which might be automated in the future.
