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Binary neural networks have attracted numerous attention in recent years.However, mainly due to the information loss stemming from the biasedbinarization, how to preserve the accuracy of networks still remains a criticalissue. In this paper, we attempt to maintain the information propagated in theforward process and propose a Balanced Binary Neural Networks with GatedResidual (BBG for short). First, a weight balanced binarization is introducedto maximize information entropy of binary weights, and thus the informativebinary weights can capture more information contained in the activations.Second, for binary activations, a gated residual is further appended tocompensate their information loss during the forward process, with a slightoverhead. Both techniques can be wrapped as a generic network module thatsupports various network architectures for different tasks includingclassification and detection. We evaluate our BBG on image classification tasksover CIFAR-10/100 and ImageNet and on detection task over Pascal VOC. Theexperimental results show that BBG-Net performs remarkably well across variousnetwork architectures such as VGG, ResNet and SSD with the superior performanceover state-of-the-art methods in terms of memory consumption, inference speedand accuracy.

Balanced Binary Neural Networks with Gated Residual