  In this paper, we introduce a new regularization technique for transfer
learning. The aim of the proposed approach is to capture statistical
relationships among convolution filters learned from a well-trained network and
transfer this knowledge to another network. Since convolution filters of the
prevalent deep Convolutional Neural Network (CNN) models share a number of
similar patterns, in order to speed up the learning procedure, we capture such
correlations by Gaussian Mixture Models (GMMs) and transfer them using a
regularization term. We have conducted extensive experiments on the CIFAR10,
Places2, and CMPlaces datasets to assess generalizability, task
transferability, and cross-model transferability of the proposed approach,
respectively. The experimental results show that the feature representations
have efficiently been learned and transferred through the proposed statistical
regularization scheme. Moreover, our method is an architecture independent
approach, which is applicable for a variety of CNN architectures.
