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Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 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.
Categorical reparameterization with gumbel-softmax
E. Jang, S. Gu, and B. Poole · 2016
Earlier work this paper cites.
The concrete distribution: A continuous relaxation of discrete random variables
C. J. Maddison, A. Mnih, and Y. W. Teh · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Condensenet: An efficient densenet using learned group convolutions
G. Huang, S. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
Earlier work this paper cites.
Progressive neural architecture search
C. Liu, B. Zoph, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Learning time/memory-efficient deep architectures with budgeted super networks
T. Veniat and L. Denoyer · 2017
Cited alongside, same era.
Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving
B. Wu, F. N. Iandola, P. H. Jin, and K. Keutzer · 2017
Cited alongside, same era.
Shift: A zero flop, zero parameter alternative to spatial convolutions
B. Wu, A. Wan, X. Yue, P. Jin, S. Zhao, N. Golmant, A. Gholaminejad, J. Gonzalez, and K. Keutzer · 2017
Shufflenet v2: Practical guidelines for efficient cnn architecture design
N. Ma, X. Zhang, H.-T. Zheng, and J. Sun · 2018
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Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
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Mnasnet: Platform-aware neural architecture search for mobile
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Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud
B. Wu, A. Wan, X. Yue, and K. Keutzer · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices. arxiv 2017
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2017
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
Cited alongside, same era.
Squeezenext: Hardware-aware neural network design
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Amc: Automl for model compression and acceleration on mobile devices
Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han · 2018
Cited alongside, same era.
Darts: Differentiable architecture search
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Mixed precision quantization of convnets via differentiable neural architecture search
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Netadapt: Platform-aware neural network adaptation for mobile applications
T.-J. Yang, A. Howard, B. Chen, X. Zhang, A. Go, M. Sandler, V. Sze, and H. Adam · 2018
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Synetgy: Algorithm-hardware co-design for convnet accelerators on embedded fpgas
Y. Yang, Q. Huang, B. Wu, T. Zhang, L. Ma, G. Gambardella, M. Blott, L. Lavagno, K. Vissers, J. Wawrzynek, et al · 2018
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Snas: stochastic neural architecture search
Anonymous · 2019
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