  We propose a modularization method that decomposes a deep neural network
(DNN) into small modules from a functionality perspective and recomposes them
into a new model for some other task. Decomposed modules are expected to have
the advantages of interpretability and verifiability due to their small size.
In contrast to existing studies based on reusing models that involve
retraining, such as a transfer learning model, the proposed method does not
require retraining and has wide applicability as it can be easily combined with
existing functional modules. The proposed method extracts modules using weight
masks and can be applied to arbitrary DNNs. Unlike existing studies, it
requires no assumption about the network architecture. To extract modules, we
designed a learning method and a loss function to maximize shared weights among
modules. As a result, the extracted modules can be recomposed without a large
increase in the size. We demonstrate that the proposed method can decompose and
recompose DNNs with high compression ratio and high accuracy and is superior to
the existing method through sharing weights between modules.
