  Training CNNs from scratch on new domains typically demands large numbers of
labeled images and computations, which is not suitable for low-power hardware.
One way to reduce these requirements is to modularize the CNN architecture and
freeze the weights of the heavier modules, that is, the lower layers after
pre-training. Recent studies have proposed alternative modular architectures
and schemes that lead to a reduction in the number of trainable parameters
needed to match the accuracy of fully fine-tuned CNNs on new domains. Our work
suggests that a further reduction in the number of trainable parameters by an
order of magnitude is possible. Furthermore, we propose that new modularization
techniques for multiple domain learning should also be compared on other
realistic metrics, such as the number of interconnections needed between the
fixed and trainable modules, the number of training samples needed, the order
of computations required and the robustness to partial mislabeling of the
training data. On all of these criteria, the proposed architecture demonstrates
advantages over or matches the current state-of-the-art.
