  Binary neural networks have attracted numerous attention in recent years.
However, mainly due to the information loss stemming from the biased
binarization, how to preserve the accuracy of networks still remains a critical
issue. In this paper, we attempt to maintain the information propagated in the
forward process and propose a Balanced Binary Neural Networks with Gated
Residual (BBG for short). First, a weight balanced binarization is introduced
to maximize information entropy of binary weights, and thus the informative
binary weights can capture more information contained in the activations.
Second, for binary activations, a gated residual is further appended to
compensate their information loss during the forward process, with a slight
overhead. Both techniques can be wrapped as a generic network module that
supports various network architectures for different tasks including
classification and detection. We evaluate our BBG on image classification tasks
over CIFAR-10/100 and ImageNet and on detection task over Pascal VOC. The
experimental results show that BBG-Net performs remarkably well across various
network architectures such as VGG, ResNet and SSD with the superior performance
over state-of-the-art methods in terms of memory consumption, inference speed
and accuracy.
