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For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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M. Lin, Q. Chen, and S. Yan · 2013
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller · 2014
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BinaryConnect: Training Deep Neural Networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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S. Han, H. Mao, and W. J. Dally · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1MB model size
F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer · 2016
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SGDR: stochastic gradient descent with restarts
I. Loshchilov and F. Hutter · 2016
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Deep neural networks are robust to weight binarization and other non-linear distortions
A downsampled variant of imagenet as an alternative to the CIFAR datasets
P. Chrabaszcz, I. Loshchilov, and F. Hutter · 2017
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Improved regularization of convolutional neural networks with cutout
T. Devries and G. W. Taylor · 2017
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X. Gastaldi · 2017
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
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P. Merolla, R. Appuswamy, J. V. Arthur, S. K. Esser, and D. S. Modha · 2016
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XNOR-Net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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Aggregated residual transformations for deep neural networks
S. Xie, R. B. Girshick, P. Dollár, Z. Tu, and K. He · 2016
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Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
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Deep learning with low precision by half-wave Gaussian quantization
Z. Cai, X. He, J. Sun, and N. Vasconcelos · 2017
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Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
K. He, X. Zhang, S. Ren, and J. Sun
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun
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A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
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Training quantized nets: A deeper understanding
H. Li, S. De, Z. Xu, C. Studer, H. Samet, and T. Goldstein · 2017
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Reduced-memory training and deployment of deep residual networks by stochastic binary quantization
M. D. McDonnell, R. Wang, and A. van Schaik · 2017
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Espresso: Efficient forward propagation for bcnns
F. Pedersoli, G. Tzanetakis, and A. Tagliasacchi · 2017
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Incremental network quantization: Towards lossless CNNs with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
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