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This paper provides an extensive analysis of the performance of the EfficientNet image classifiers with several recent training procedures, in particular one that corrects the discrepancy between train and test images.
“Imagenet large scale visual recognition challenge,”
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei, · 2015
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Autoaugment: Learning augmentation policies from data,”
Ekin Dogus Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V. Le, · 2018
Earlier work this paper cites.
“Gpipe: Efficient training of giant neural networks using pipeline parallelism,”
Yanping Huang, Yonglong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, and Zhifeng Chen, · 2018
Earlier work this paper cites.
“Exploring the limits of weakly supervised pretraining,”
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten, · 2018
Earlier work this paper cites.
“Learning transferable architectures for scalable image recognition,”
Barret Zoph, V. Vasudevan, Jonathon Shlens, and Quoc V. Le, · 2018
Earlier work this paper cites.
“Progressive neural architecture search,”
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy, · 2018
Cited alongside, same era.
“Fixing the train-test resolution discrepancy,”
Hugo Touvron, Andrea Vedaldi, Matthijs Douze, and Hérve Jégou, · 2019
Cited alongside, same era.
“Billion-scale semi-supervised learning for image classification,”
Ismet Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, and Dhruv Kumar Mahajan, · 2019
Cited alongside, same era.
“Efficientnet: Rethinking model scaling for convolutional neural networks,”
Mingxing Tan and Quoc V. Le, · 2019
Cited alongside, same era.
“Self-training with noisy student improves imagenet classification,”
Qizhe Xie, Eduard H. Hovy, Minh-Thang Luong, and Quoc V. Le, · 2019
“Randaugment: Practical automated data augmentation with a reduced search space,”
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le, · 2019
Later among the works it cites.
“Do imagenet classifiers generalize to imagenet?,”
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar, · 2019
Later among the works it cites.
Lucas Beyer, Olivier J. Hénaff, A. Kolesnikov, Xiaohua Zhai, and Aaron van den Oord, · 2020
Closest in time.
Accessed: 2020-03-01
“Pre-trained efficientnet models,” https://github.com/rwightman/pytorch-image-models/ · 2020
Closest in time.
“Designing network design spaces,”
Ilija Radosavovic, Raj Prateek Kosaraju, Ross B. Girshick, Kaiming He, and Piotr Dollár, · 2020
Closest in time.
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Cited alongside, same era.
“Adversarial examples improve image recognition,”
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le, · 2019
Cited alongside, same era.