  Deep convolutional neural networks (CNNs) have shown appealing performance on
various computer vision tasks in recent years. This motivates people to deploy
CNNs to realworld applications. However, most of state-of-art CNNs require
large memory and computational resources, which hinders the deployment on
mobile devices. Recent studies show that low-bit weight representation can
reduce much storage and memory demand, and also can achieve efficient network
inference. To achieve this goal, we propose a novel approach named BWNH to
train Binary Weight Networks via Hashing. In this paper, we first reveal the
strong connection between inner-product preserving hashing and binary weight
networks, and show that training binary weight networks can be intrinsically
regarded as a hashing problem. Based on this perspective, we propose an
alternating optimization method to learn the hash codes instead of directly
learning binary weights. Extensive experiments on CIFAR10, CIFAR100 and
ImageNet demonstrate that our proposed BWNH outperforms current state-of-art by
a large margin.
