  Deep convolutional neural networks have been widely used in numerous
applications, but their demanding storage and computational resource
requirements prevent their applications on mobile devices. Knowledge
distillation aims to optimize a portable student network by taking the
knowledge from a well-trained heavy teacher network. Traditional
teacher-student based methods used to rely on additional fully-connected layers
to bridge intermediate layers of teacher and student networks, which brings in
a large number of auxiliary parameters. In contrast, this paper aims to
propagate information from teacher to student without introducing new variables
which need to be optimized. We regard the teacher-student paradigm from a new
perspective of feature embedding. By introducing the locality preserving loss,
the student network is encouraged to generate the low-dimensional features
which could inherit intrinsic properties of their corresponding
high-dimensional features from teacher network. The resulting portable network
thus can naturally maintain the performance as that of the teacher network.
Theoretical analysis is provided to justify the lower computation complexity of
the proposed method. Experiments on benchmark datasets and well-trained
networks suggest that the proposed algorithm is superior to state-of-the-art
teacher-student learning methods in terms of computational and storage
complexity.
