  In this paper, we propose to train convolutional neural networks (CNNs) with
both binarized weights and activations, leading to quantized models
specifically} for mobile devices with limited power capacity and computation
resources. Previous works on quantizing CNNs seek to approximate the
floating-point information using a set of discrete values, which we call value
approximation, but typically assume the same architecture as the full-precision
networks. In this paper, however, we take a novel 'structure approximation'
view for quantization---it is very likely that a different architecture may be
better for best performance. In particular, we propose a `network
decomposition' strategy, named \textbf{Group-Net}, in which we divide the
network into groups. In this way, each full-precision group can be effectively
reconstructed by aggregating a set of homogeneous binary branches.
  In addition, we learn effective connections among groups to improve the
representational capability. Moreover, the proposed Group-Net shows strong
generalization to other tasks. For instance, we extend Group-Net for highly
accurate semantic segmentation by embedding rich context into the binary
structure.
  Experiments on both classification and semantic segmentation tasks
demonstrate the superior performance of the proposed methods over various
popular architectures. In particular, we outperform the previous best binary
neural networks in terms of accuracy and major computation savings.

Computer Vision and Pattern Recognition