  The advancement of deep convolutional neural networks (DCNNs) has driven
significant improvement in the accuracy of recognition systems for many
computer vision tasks. However, their practical applications are often
restricted in resource-constrained environments. In this paper, we introduce
projection convolutional neural networks (PCNNs) with a discrete back
propagation via projection (DBPP) to improve the performance of binarized
neural networks (BNNs). The contributions of our paper include: 1) for the
first time, the projection function is exploited to efficiently solve the
discrete back propagation problem, which leads to a new highly compressed CNNs
(termed PCNNs); 2) by exploiting multiple projections, we learn a set of
diverse quantized kernels that compress the full-precision kernels in a more
efficient way than those proposed previously; 3) PCNNs achieve the best
classification performance compared to other state-of-the-art BNNs on the
ImageNet and CIFAR datasets.
