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Improving model performance is always the key problem in machine learning including deep learning.
“Ensemble learning via negative correlation,”
Yong Liu and Xin Yao, · 1999
Earlier work this paper cites.
“Random forests,”
Leo Breiman, · 2001
Earlier work this paper cites.
“Ensemble selection from libraries of models,”
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, and Alex Ksikes, · 2004
Earlier work this paper cites.
“Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,”
Li Fei-Fei, Rob Fergus, and Pietro Perona, · 2007
Earlier work this paper cites.
“Reading digits in natural images with unsupervised feature learning,”
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng, · 2011
Earlier work this paper cites.
“Fine-grained visual classification of aircraft,”
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi, · 2013
Earlier work this paper cites.
“Food-101 – mining discriminative components with random forests,”
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool, · 2014
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.
“Matching networks for one shot learning,”
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al., · 2016
Earlier work this paper cites.
“Inception-v4, inception-resnet and the impact of residual connections on learning,”
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi, · 2017
Cited alongside, same era.
“Densely connected convolutional networks,”
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger, · 2017
Cited alongside, same era.
“Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,”
Han Xiao, Kashif Rasul, and Roland Vollgraf, · 2017
Cited alongside, same era.
“Incorporating intra-class variance to fine-grained visual recognition,”
Yan Em, Feng Gag, Yihang Lou, Shiqi Wang, Tiejun Huang, and Ling-Yu Duan, · 2017
Cited alongside, same era.
“Improved regularization of convolutional neural networks with cutout,”
Terrance DeVries and Graham W Taylor, · 2017
Cited alongside, same era.
Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, and Thomas Huang, · 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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“Learning transferable architectures for scalable image recognition,”
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le, · 2018
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“Regularized evolution for image classifier architecture search,”
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le, · 2018
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“Irlas: Inverse reinforcement learning for architecture search,”
Minghao Guo, Zhao Zhong, Wei Wu, Dahua Lin, and Junjie Yan, · 2018
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“Smash: one-shot model architecture search through hypernetworks,”
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston, · 2017
Cited alongside, same era.
“Path aggregation network for instance segmentation,”
Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia, · 2018
Cited alongside, same era.
“Understanding and simplifying one-shot architecture search,”
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le, · 2018
Cited alongside, same era.
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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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“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
Later among the works it cites.
“Darts: Differentiable architecture search,”
Hanxiao Liu, Karen Simonyan, and Yiming Yang, · 2019
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