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Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture.
ImageNet: A large-scale hierarchical image database
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Alex Krizhevsky and Geoffrey Hinton · 2009
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ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Dropout: A simple way to prevent neural networks from overfitting
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Adam: A method for stochastic optimization
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Going deeper with convolutions
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Hypernetworks
David Ha, Andrew Dai, and Quoc V Le · 2017
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MobileNets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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FractalNet: Ultra-deep neural networks without residuals
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2017
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V Le, and Alexey Kurakin · 2017
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Genetic CNN
Lingxi Xie and Alan Yuille · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
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SMASH: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
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ProxylessNAS: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
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Probabilistic neural architecture search
Francesco Paolo Casale, Jonathan Gordon, and Nicolo Fusi · 2019
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Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian · 2019
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Efficient multi-objective neural architecture search via lamarckian evolution
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Efficient architecture search by network transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang · 2018
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Neural architecture optimization
Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu · 2018
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ShuffleNet V2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Pelee: A real-time object detection system on mobile devices
Robert J. Wang, Xiang Li, Shuang Ao, and Charles X. Ling · 2018
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Liam Li and Ameet Talwalkar · 2019
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DARTS: Differentiable architecture search
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Evolving deep neural networks
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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Evaluating the search phase of neural architecture search
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MnasNet: Platform-aware neural architecture search for mobile
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SNAS: Stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
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BayesNAS: A Bayesian approach for neural architecture search
Hongpeng Zhou, Minghao Yang, Jun Wang, and Wei Pan · 2019
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AtomNAS: Fine-grained end-to-end neural architecture search
Jieru Mei, Xiaochen Lian, Xiaojie Jin, Linjie Yang, Yingwei Li, Alan Yuille, and Jianchao Yang · 2020
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