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.

通过量化模型，将卷积神经网络结构进行近似重构，创新性地提出群组分解策略（Group-Net），并嵌入上下文信息以进行准确的语义分割，首次将二进制神经网络应用于目标检测，展示了在精度和计算效率方面优于文献中各种量化网络的方法。

结构化二进制神经网络用于图像识别

Structured Binary Neural Networks for Image Recognition

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.

本文提出一种新的 CNNs 量化方法，基于二进制权重和激活值，结合 “结构近似” 的思想使用 “Group-Net” 策略，将网络分组，将同类别的二进制分支聚合为一个完整的分组，同时学习组之间的有效连接，在移动设备上达到了较好的分类与语义分割性能，节约了大量的计算资源。