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In this paper, we propose to train a network with binary weights and low-bitwidth activations, designed especially for mobile devices with limited power consumption.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Léonard, and A. Courville · 2013
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2015
Earlier work this paper cites.
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, et al · 2015
Earlier work this paper cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Earlier work this paper cites.
An analysis of deep neural network models for practical applications
A. Canziani, A. Paszke, and E. Culurciello · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Binarized neural networks
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2016
Earlier work this paper cites.
F. Li, B. Zhang, and B. Liu · 2016
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You only look once: Unified, real-time object detection
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou · 2016
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 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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Trained ternary quantization
C. Zhu, S. Han, H. Mao, and W. J. Dally · 2017
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Neural architecture search with reinforcement learning
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Loss-aware binarization of deep networks
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Hierarchical representations for efficient architecture search
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