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Neural architecture search (NAS) has shown great promise in designing state-of-the-art (SOTA) models that are both accurate and efficient.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Collecting a large-scale dataset of fine-grained cars
Jonathan Krause, Jia Deng, Michael Stark, and Li Fei-Fei · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
Earlier work this paper cites.
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.
Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, and Alex Kurakin · 2017
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Genetic cnn
Lingxi Xie and Alan Yuille · 2017
Earlier work this paper cites.
Proxylessnas: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2018
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Searching toward pareto-optimal device-aware neural architectures
An-Chieh Cheng, Jin-Dong Dong, Chi-Hung Hsu, Shu-Huan Chang, Min Sun, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, and Da-Cheng Juan · 2018
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, Yonghui Wu, et al · 2018
Earlier work this paper cites.
Unsupervised hard example mining from videos for improved object detection
SouYoung Jin, Aruni RoyChowdhury, Huaizu Jiang, Ashish Singh, Aditya Prasad, Deep Chakraborty, and Erik Learned-Miller · 2018
Earlier work this paper cites.
Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Hard example mining with auxiliary embeddings
Evgeny Smirnov, Aleksandr Melnikov, Andrei Oleinik, Elizaveta Ivanova, Ilya Kalinovskiy, and Eugene Luckyanets · 2018
Cited alongside, same era.
Masanori Suganuma, Mete Ozay, and Takayuki Okatani · 2018
Stochastic class-based hard example mining for deep metric learning
Yumin Suh, Bohyung Han, Wonsik Kim, and Kyoung Mu Lee · 2019
Later among the works it cites.
Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
Later among the works it cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
Later among the works it cites.
Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2019
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Nas evaluation is frustratingly hard
Antoine Yang, Pedro M Esperança, and Fabio M Carlucci · 2019
Later among the works it cites.
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Cited alongside, same era.
Practical block-wise neural network architecture generation
Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, and Cheng-Lin Liu · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Cited alongside, same era.
A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
Cited alongside, same era.
Once-for-all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2019
Cited alongside, same era.
Generalization bounds of stochastic gradient descent for wide and deep neural networks
Yuan Cao and Quanquan Gu · 2019
Cited alongside, same era.
Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search
Xiangxiang Chu, Bo Zhang, Ruijun Xu, and Jixiang Li · 2019
Cited alongside, same era.
Chamnet: Towards efficient network design through platform-aware model adaptation
Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, et al · 2019
Cited alongside, same era.
Nas-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, and Frank Hutter · 2019
Later among the works it cites.
Universally slimmable networks and improved training techniques
Jiahui Yu and Thomas S Huang · 2019
Later among the works it cites.
Pareco: Pareto-aware channel optimization for slimmable neural networks
Ting-Wu Chin, Ari S Morcos, and Diana Marculescu · 2020
Closest in time.
Fbnetv3: Joint architecture-recipe search using neural acquisition function
Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, et al · 2020
Closest in time.
Nas-bench-102: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Closest in time.
Maxup: A simple way to improve generalization of neural network training
Chengyue Gong, Tongzheng Ren, Mao Ye, and Qiang Liu · 2020
Closest in time.
Single path one-shot neural architecture search with uniform sampling
Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, and Jian Sun · 2020
Closest in time.
Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions
Alvin Wan, Xiaoliang Dai, Peizhao Zhang, Zijian He, Yuandong Tian, Saining Xie, Bichen Wu, Matthew Yu, Tao Xu, Kan Chen, et al · 2020
Closest in time.
Hat: Hardware-aware transformers for efficient natural language processing
Hanrui Wang, Zhanghao Wu, Zhijian Liu, Han Cai, Ligeng Zhu, Chuang Gan, and Song Han · 2020
Closest in time.
Bignas: Scaling up neural architecture search with big single-stage models
Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, and Quoc Le · 2020
Closest in time.