  Increasing number of filters in deeper layers when feature maps are decreased
is a widely adopted pattern in convolutional network design. It can be found in
classical CNN architectures and in automatic discovered models. Even CNS
methods commonly explore a selection of multipliers derived from this pyramidal
pattern. We defy this practice by introducing a small set of templates
consisting of easy to implement, intuitive and aggressive variations of the
original pyramidal distribution of filters in VGG and ResNet architectures.
Experiments on CIFAR, CINIC10 and TinyImagenet datasets show that models
produced by our templates, are more efficient in terms of fewer parameters and
memory needs.
