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Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate.
Multi-objective optimization
K. Deb · 2014
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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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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, et al · 2015
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 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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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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SSD: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
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Designing neural network architectures using reinforcement learning
B. Baker, O. Gupta, N. Naik, and R. Raskar · 2017
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Accurate, large minibatch sgd: training imagenet in 1 hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 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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Yolo9000: better, faster, stronger
J. Redmon and A. Farhadi · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2017
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DPP-Net: Device-aware progressive search for pareto-optimal neural architectures
J.-D. Dong, A.-C. Cheng, D.-C. Juan, W. Wei, and M. Sun · 2018
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Multi-objective architecture search for cnns
T. Elsken, J. H. Metzen, and F. Hutter · 2018
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Squeezenext: Hardware-aware neural network design
A. Gholami, K. Kwon, B. Wu, Z. Tai, X. Yue, P. Jin, S. Zhao, and K. Keutzer · 2018
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Morphnet: Fast & simple resource-constrained structure learning of deep networks
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 · 2018
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Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu · 2018
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Neural architecture optimization
R. Luo, F. Tian, T. Qin, and T.-Y. Liu · 2018
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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
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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A. Gordon, E. Eban, O. Nachum, B. Chen, H. Wu, T.-J. Yang, and E. Choi · 2018
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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.
MONAS: Multi-objective neural architecture search using reinforcement learning
C.-H. Hsu, S.-H. Chang, D.-C. Juan, J.-Y. Pan, Y.-T. Chen, W. Wei, and S.-C. Chang · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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Condensenet: An efficient densenet using learned group convolutions
G. Huang, S. Liu, L. van der Maaten, and K. Q. Weinberger · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko · 2018
Cited alongside, same era.
Neural architecture search with bayesian optimisation and optimal transport
K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, and E. Xing · 2018
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Netadapt: Platform-aware neural network adaptation for mobile applications
T.-J. Yang, A. Howard, B. Chen, X. Zhang, A. Go, V. Sze, and H. Adam · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2018
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Resource-efficient neural architect
Y. Zhou, S. Ebrahimi, S. Ö. Arık, H. Yu, H. Liu, and G. Diamos · 2018
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2018
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DARTS: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2019
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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2019
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