  Deep neural networks (DNNs) have achieved great success in a wide range of
computer vision areas, but the applications to mobile devices is limited due to
their high storage and computational cost. Much efforts have been devoted to
compress DNNs. In this paper, we propose a simple yet effective method for deep
networks compression, named Cluster Regularized Quantization (CRQ), which can
reduce the presentation precision of a full-precision model to ternary values
without significant accuracy drop. In particular, the proposed method aims at
reducing the quantization error by introducing a cluster regularization term,
which is imposed on the full-precision weights to enable them naturally
concentrate around the target values. Through explicitly regularizing the
weights during the re-training stage, the full-precision model can achieve the
smooth transition to the low-bit one. Comprehensive experiments on benchmark
datasets demonstrate the effectiveness of the proposed method.
