二值神经网络（Binary Neural Network，BNN）

港控/mmm° 2022-02-16 10:18 371阅读 0赞

转载自多篇博客，仅用作个人学习，如需删除，请见谅并联系本人。

# **BNN** #

Bengio大神的著作《Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to +1 or −1》：

链接：https://arxiv.org/abs/1602.02830

参考代码：https://github.com/MatthieuCourbariaux/BinaryNet

参考博文：[https://blog.csdn.net/lily\_9/article/details/81409249][https_blog.csdn.net_lily_9_article_details_81409249]

[https://blog.csdn.net/stdcoutzyx/article/details/50926174][https_blog.csdn.net_stdcoutzyx_article_details_50926174]

# XNOR-Net算法 #

博客：[https://blog.csdn.net/u014380165/article/details/77731595][https_blog.csdn.net_u014380165_article_details_77731595]

论文：XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks  
链接：https://arxiv.org/abs/1603.05279  
代码地址：http://allenai.org/plato/xnornet

#    Bi-Real Net——XNOR-net的增强与演进 #

Bi-Real Net: Enhancing the Performance of 1bit CNNs with Improved Representational Capacity and Advanced Training Algorithm

博客：https://blog.csdn.net/nature553863/article/details/82497777

Paper地址：https://arxiv.org/abs/1808.00278v5

GitHub（Bi-Real net，Caffe）：https://github.com/liuzechun/Bi-Real-net

GitHub（XNOR-net，PyTorch）：https://github.com/jiecaoyu/XNOR-Net-PyTorch

# ABCnet #

Towards Accurate Binary Convolutional Neural Network"这篇文章提出了ABCnet，是一种表示精度较高的二值化网络结构（作为XNORnet的演进）。有关XNORnet及其优势可以参考论文："XNORNet: ImageNet Classification Using Binary Convolutional Neural Networks"。

博客：https://blog.csdn.net/nature553863/article/details/80653521

论文地址：https://arxiv.org/abs/1711.11294

ABCnet GitHub：https://github.com/layog/Accurate-Binary-Convolution-Network

XNORnet GitHub：https://github.com/ayush29feb/Sketch-A-XNORNet

[https_blog.csdn.net_lily_9_article_details_81409249]: https://blog.csdn.net/lily_9/article/details/81409249
[https_blog.csdn.net_stdcoutzyx_article_details_50926174]: https://blog.csdn.net/stdcoutzyx/article/details/50926174
[https_blog.csdn.net_u014380165_article_details_77731595]: https://blog.csdn.net/u014380165/article/details/77731595