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Much research work in computer vision is being spent on optimizing existing network architectures to obtain a few more percentage points on benchmarks.
Evolving neural networks through augmenting topologies
Kenneth O. Stanley and Risto Miikkulainen · 2002
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Stereo processing by semiglobal matching and mutual information
Heiko Hirschmuller · 2008
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Stereo processing by semiglobal matching and mutual information
Heiko Hirschmüller · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Algorithms for hyper-parameter optimization
James S. Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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A naturalistic open source movie for optical flow evaluation
Daniel J. Butler, Jonas Wulff, Garrett B. Stanley, and Michael J. Black · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P. Adams · 2012
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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Cox · 2013
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An evaluation of sequential model-based optimization for expensive blackbox functions
Frank Hutter, Holger Hoos, and Kevin Leyton-Brown · 2013
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2014
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An efficient approach for assessing hyperparameter importance
Frank Hutter, Holger Hoos, and Kevin Leyton-Brown · 2014
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Phillip Fischer, Eddy Ilg, Phillip Häusser, Caner Hazırbaş, Vladamir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Phillip Fischer, and Thomas Brox · 2015
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Scalable bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams · 2015
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Non-stochastic best arm identification and hyperparameter optimization
Kevin Jamieson and Ameet Talwalkar · 2016
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Deeper depth prediction with fully convolutional residual networks
Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, and Nassir Navab · 2016
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Efficient deep learning for stereo matching
Wenjie Luo, Alexander G. Schwing, and Raquel Urtasun · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Nikolaus Mayer, Eddy Ilg, Philip Häusser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
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Convolutional neural fabrics
Shreyas Saxena and Jakob Verbeek · 2016
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Taking the human out of the loop: A review of bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas · 2016
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Stereo matching by training a convolutional neural network to compare image patches
Jure Zbontar and Yann LeCun · 2016
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Gated feedback refinement network for dense image labeling
Md Amirul Islam, Mrigank Rochan, Neil D. B. Bruce, and Yang Wang · 2017
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
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Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2017
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Accelerating Neural Architecture Search using Performance Prediction
Bowen Baker, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik · 2017
Cited alongside, same era.
Neural optimizer search with reinforcement learning
Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V. Le · 2017
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
Cited alongside, same era.
Simple And Efficient Architecture Search for Convolutional Neural Networks
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 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.
Flownet 2.0: Evolution of optical flow estimation with deep networks
Hyperparameter optimization
Matthias Feurer and Frank Hutter · 2018
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Shuffleseg: Real-time semantic segmentation network
Mostafa Gamal, Mennatullah Siam, and Moemen Abdel-Razek · 2018
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Automatic Machine Learning: Methods, Systems, Challenges
Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren, editors · 2018
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Occlusions, motion and depth boundaries with a generic network for disparity, optical flow or scene flow estimation
Eddy Ilg, Tonmoy Saikia, Margret Keuper, and Thomas Brox · 2018
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh Rostamizadeh, and Ameet Talwalkar · 2018
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Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
Cited alongside, same era.
End-to-end learning of geometry and context for deep stereo regression
Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry · 2017
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017
Cited alongside, same era.
Cascade residual learning: A two-stage convolutional neural network for stereo matching
Jiahao Pang, Wenxiu Sun, Jimmy SJ Ren, Chengxi Yang, and Qiong Yan · 2017
Cited alongside, same era.
Optical flow estimation using a spatial pyramid network
Anurag Ranjan and Michael Black · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, and Jianfeng Zhang · 2018
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Hierarchical representations for efficient architecture search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu · 2018
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Evolving deep neural networks
Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, and Babak Hodjat · 2018
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Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 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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Edgestereo: A context integrated residual pyramid network for stereo matching
Xiao Song, Xu Zhao, Hanwen Hu, and Liangji Fang · 2018
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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
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Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, and Quoc V. Le · 2018
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Segstereo: Exploiting semantic information for disparity estimation
Guorun Yang, Hengshuang Zhao, Jianping Shi, Zhidong Deng, and Jiaya Jia · 2018
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Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
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Exfuse: Enhancing feature fusion for semantic segmentation
Zhenli Zhang, Xiangyu Zhang, Chao Peng, Xiangyang Xue, and Jian Sun · 2018
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Practical block-wise neural network architecture generation
Zhao Zhong, Jingchen Yan, Wei Wu, Jing Shao, and Cheng-Lin Liu · 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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Efficient multi-objective neural architecture search via lamarckian evolution
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan Yuille, and Li Fei-Fei · 2019
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DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Learning to design RNA
Frederic Runge, Danny Stoll, Stefan Falkner, and Frank Hutter · 2019
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SNAS: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
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Graph hypernetworks for neural architecture search
Chris Zhang, Mengye Ren, and Raquel Urtasun · 2019
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