  How to effectively approximate real-valued parameters with binary codes plays
a central role in neural network binarization. In this work, we reveal an
important fact that binarizing different layers has a widely-varied effect on
the compression ratio of network and the loss of performance. Based on this
fact, we propose a novel and flexible neural network binarization method by
introducing the concept of layer-wise priority which binarizes parameters in
inverse order of their layer depth. In each training step, our method selects a
specific network layer, minimizes the discrepancy between the original
real-valued weights and its binary approximations, and fine-tunes the whole
network accordingly. During the iteration of the above process, it is
significant that we can flexibly decide whether to binarize the remaining
floating layers or not and explore a trade-off between the loss of performance
and the compression ratio of model. The resulting binary network is applied for
efficient pedestrian detection. Extensive experimental results on several
benchmarks show that under the same compression ratio, our method achieves much
lower miss rate and faster detection speed than the state-of-the-art neural
network binarization method.
