  In recent times, using small data to train networks has become a hot topic in
the field of deep learning. Reusing pre-trained parameters is one of the most
important strategies to address the issue of semi-supervised and transfer
learning. However, the fundamental reason for the success of these methods is
still unclear. In this paper, we propose a solution that can not only judge
whether a given network is reusable or not based on the performance of reusing
convolution kernels but also judge which layers' parameters of the given
network can be reused, based on the performance of reusing corresponding
parameters and, ultimately, judge whether those parameters are reusable or not
in a target task based on the root mean square error (RMSE) of the
corresponding convolution kernels. Specifically, we define that the success of
a CNN's parameter reuse depends upon two conditions: first, the network is a
reusable network; and second, the RMSE between the convolution kernels from the
source domain and target domain is small enough. The experimental results
demonstrate that the performance of reused parameters applied to target tasks,
when these conditions are met, is significantly improved.
