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We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs).
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Imagenet large scale visual recognition challenge
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
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Deep residual learning for image recognition
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Quantized convolutional neural networks for mobile devices
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Aggregated residual transformations for deep neural networks
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
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J. Hu, L. Shen, and G. Sun · 2017
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Swish: a self-gated activation function
P. Ramachandran, B. Zoph, and Q. V. Le · 2017
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T. Zhang, G.-J. Qi, B. Xiao, and J. Wang · 2017
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A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
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