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Depthwise convolution and grouped convolution has been successfully applied to improve the efficiency of convolutional neural network (CNN).
Receptive fields and functional architecture of monkey striate cortex
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Optimal brain damage
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Torch7: A matlab-like environment for machine learning
R. Collobert, K. Kavukcuoglu, and C. Farabet · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
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Fast training of convolutional networks through ffts
M. Mathieu, M. Henaff, and Y. LeCun · 2013
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Exploiting linear structure within convolutional networks for efficient evaluation
E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. B. Girshick, S. Guadarrama, and T. Darrell · 2014
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
V. Lebedev, Y. Ganin, M. Rakhuba, I. V. Oseledets, and V. S. Lempitsky · 2014
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S. Han, H. Mao, and W. J. Dally · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 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
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
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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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Factorized convolutional neural networks
M. Wang, B. Liu, and H. Foroosh · 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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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, and et al · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 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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