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Neural Architecture Search (NAS) is an important yet challenging task in network design due to its high computational consumption.
Designing neural networks using genetic algorithms
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A new evolutionary system for evolving artificial neural networks
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Multi-agent reinforcement learning with genetic programming
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Imagenet: A large-scale hierarchical image database
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Learning multiple layers of features from tiny images
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Semantic contours from inverse detectors
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Imagenet classification with deep convolutional neural networks
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Microsoft COCO: common objects in context
T. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Net2net: Accelerating learning via knowledge transfer
T. Chen, I. J. Goodfellow, and J. Shlens · 2015
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The pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. J. V. Gool, C. K. I. Williams, J. M. Winn, and A. Zisserman · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Designing neural network architectures using reinforcement learning
B. Baker, O. Gupta, N. Naik, and R. Raskar · 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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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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Genetic CNN
L. Xie and A. L. Yuille · 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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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
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Efficient architecture search by network transformation
H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang · 2018
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C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2016
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Accelerating neural architecture search using performance prediction
B. Baker, O. Gupta, R. Raskar, and N. Naik · 2017
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Rethinking atrous convolution for semantic image segmentation
L. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
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Improved regularization of convolutional neural networks with cutout
T. Devries and G. W. Taylor · 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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Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
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L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
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Progressive neural architecture search
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. L. Yuille, J. Huang, and K. Murphy · 2018
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DARTS: differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2018
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Shufflenet V2: practical guidelines for efficient CNN architecture design
N. Ma, X. Zhang, H. 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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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2018
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M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen · 2018
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Practical block-wise neural network architecture generation
Z. Zhong, J. Yan, W. Wu, J. Shao, and C.-L. Liu · 2018
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