Exploiting Convolution Filter Patterns for Transfer Learning

Mehmet Aygun, Yusuf Aytar, Hazim Kemal Ekenel; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2674-2680

In this paper, we introduce a new regularization tech- nique for transfer learning. The aim of the proposed ap- proach is to capture statistical relationships among convo- lution filters learned from a well-trained network and trans- fer 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 cor- relations by Gaussian Mixture Models (GMMs) and trans- fer them using a regularization term. 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 indepen- dent approach, which is applicable for a variety of CNN architectures.