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There is a significant performance gap between Binary Neural Networks (BNNs) and floating point Deep Neural Networks (DNNs).
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Song Han, Huizi Mao, and William J. Dally · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Bridging nonlinearities and stochastic regularizers with gaussian error linear units
Dan Hendrycks and Kevin Gimpel · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Cited alongside, same era.
Chenzhuo Zhu, Song Han, Huizi Mao, and William J. Dally · 2016
Cited alongside, same era.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V. Le · 2017
Cited alongside, same era.
How to train a compact binary neural network with high accuracy?
Wei Tang, Gang Hua, and Liang Wang · 2017
Cited alongside, same era.
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2018
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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2018
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Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm
Zechun Liu, Baoyuan Wu, Wenhan Luo, Xin Yang, Wei Liu, and Kwang-Ting Cheng · 2018
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Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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