  We propose methods to train convolutional neural networks (CNNs) with both
binarized weights and activations, leading to quantized models that are
specifically friendly to mobile devices with limited power capacity and
computation resources. Previous works on quantizing CNNs often seek to
approximate the floating-point information using a set of discrete values,
which we call value approximation, typically assuming the same architecture as
the full-precision networks. Here we take a novel "structure approximation"
view of quantization -- it is very likely that different architectures designed
for low-bit networks may be better for achieving good performance. In
particular, we propose a "network decomposition" strategy, termed Group-Net, in
which we divide the network into groups. Thus, 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
representation capability. Moreover, the proposed Group-Net shows strong
generalization to other tasks. For instance, we extend Group-Net for accurate
semantic segmentation by embedding rich context into the binary structure.
Furthermore, for the first time, we apply binary neural networks to object
detection. Experiments on both classification, semantic segmentation and object
detection tasks demonstrate the superior performance of the proposed methods
over various quantized networks in the literature. Our methods outperform the
previous best binary neural networks in terms of accuracy and computation
efficiency.
