  Binary neural networks have great resource and computing efficiency, while
suffer from long training procedure and non-negligible accuracy drops, when
comparing to the full-precision counterparts. In this paper, we propose the
composite binary decomposition networks (CBDNet), which first compose
real-valued tensor of each layer with a limited number of binary tensors, and
then decompose some conditioned binary tensors into two low-rank binary
tensors, so that the number of parameters and operations are greatly reduced
comparing to the original ones. Experiments demonstrate the effectiveness of
the proposed method, as CBDNet can approximate image classification network
ResNet-18 using 5.25 bits, VGG-16 using 5.47 bits, DenseNet-121 using 5.72
bits, object detection networks SSD300 using 4.38 bits, and semantic
segmentation networks SegNet using 5.18 bits, all with minor accuracy drops.
