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https://github.com/michaeltinsley/awesome-binary-neural-networks

A curated list of binary neural network research papers and software packages.
https://github.com/michaeltinsley/awesome-binary-neural-networks

List: awesome-binary-neural-networks

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A curated list of binary neural network research papers and software packages.

Awesome Lists containing this project

README 

        
# Awesome Binary Neural Networks



[![Awesome][awesome-badge]][awesome-link]



> A curated list of binary neural network research papers and software packages.



## Table of Contents



- [Research Papers](#research-papers)

- [Software and Repositories](#software-and-repositories)

- [Organisations](#organisations)

- [Relevant Awesome Lists](#relevant-awesome-lists)

- [Contribute](#contribute)

- [Credits](#credits)

- [License](#license)



## Research Papers



This section contains research paper in chronological order.



- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://arxiv.org/abs/1511.00363)

    - 2015

    - Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David

- [Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1](https://arxiv.org/abs/1602.02830)

    - 2016

    - Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio

- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://arxiv.org/abs/1603.05279)

    - 2016

    - Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi

- [Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing](https://arxiv.org/abs/1603.08270)

    - 2016

    - Steven K. Esser, Paul A. Merolla, John V. Arthur, Andrew S. Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J. Berg, Jeffrey L. McKinstry, Timothy Melano, Davis R. Barch, Carmelo di Nolfo, Pallab Datta, Arnon Amir, Brian Taba, Myron D. Flickner, Dharmendra S. Modha

- [Ternary Weight Networks](https://arxiv.org/abs/1605.04711)

    - 2016

    - Fengfu Li, Bo Zhang, Bin Liu

- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://arxiv.org/abs/1606.06160)

    - 2016

    - Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, Yuheng Zou

- [Flexible Network Binarization with Layer-wise Priority](https://arxiv.org/abs/1709.04344)

    - 2017

    - Lixue Zhuang, Yi Xu, Bingbing Ni, Hongteng Xu

- [ReBNet: Residual Binarized Neural Network](https://arxiv.org/abs/1711.01243)

    - 2017

    - Mohammad Ghasemzadeh, Mohammad Samragh, Farinaz Koushanfar

- [Towards Accurate Binary Convolutional Neural Network](https://arxiv.org/abs/1711.11294)

    - 2017

    - Xiaofan Lin, Cong Zhao, Wei Pan

- [Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving](https://arxiv.org/abs/1804.06332)

    - 2018

    - Jiaolong Xu, Peng Wang, Heng Yang, Antonio M. López

- [Self-Binarizing Networks](https://arxiv.org/abs/1902.00730)

    - 2019

    - Fayez Lahoud, Radhakrishna Achanta, Pablo Márquez-Neila, Sabine Süsstrunk

- [Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization](https://arxiv.org/abs/1906.02107)

    - 2019

    - Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, Roeland Nusselder

- [Least squares binary quantization of neural networks](https://arxiv.org/abs/2001.02786v1)

    - 2020

    - Hadi Pouransari, Oncel Tuzel

- [Widening and Squeezing: Towards Accurate and Efficient QNNs](https://arxiv.org/abs/2002.00555)

    - 2020

    - Chuanjian Liu, Kai Han, Yunhe Wang, Hanting Chen, Chunjing Xu, Qi Tian

- [MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?](https://arxiv.org/abs/2001.05936)

    - 2020

    - Joseph Bethge, Christian Bartz, Haojin Yang, Ying Chen, Christoph Meinel



## Software and Repositories



- [Larq](https://github.com/larq/larq) - An open-source deep learning library based on the `tf.keras` interface.



## Organisations



- [Plumerai](https://www.plumerai.com/) - Plumerai is enabling devices like robots and drones to use deep learning locally and in real-time with binarised neural networks.



## Relevant Awesome Lists



* [awesome-awesome](https://github.com/emijrp/awesome-awesome)

* [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness)

* [sindresorhus/awesome](https://github.com/sindresorhus/awesome)

* [The Warren](https://github.com/torchhound/warren)



## Contribute



Contributions welcome! Read the [contribution guidelines](CONTRIBUTING.md) first.



## Credits



See [AUTHORS](AUTHORS.md)



This project was initially created with [Cookiecutter][cookiecutter] and the custom [cookiecutter-awesome][cookiecutter-awesome] :cookie:



## License



[![CC0][CC0-badge]][CC0-link]



To the extent possible under law, Michael Tinsley has waived all copyright

and related or neighboring rights to this work. See [LICENSE](LICENSE).



[awesome-badge]: https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg

[awesome-link]: https://github.com/sindresorhus/awesome

[CC0-badge]: http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg

[CC0-link]: https://creativecommons.org/publicdomain/zero/1.0/

[cookiecutter]: https://github.com/audreyr/cookiecutter

[cookiecutter-awesome]: https://github.com/moodule/cookiecutter-git