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In the low-data regime, it is difficult to train good supervised models from scratch.
Ensemble selection from libraries of models
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, and Alex Ksikes · 2004
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Maria-Elena Nilsback and Andrew Zisserman · 2006
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Ensemble methods in data mining: improving accuracy through combining predictions
Giovanni Seni and John F Elder · 2010
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Deep learning of representations for unsupervised and transfer learning
Yoshua Bengio · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, and Andrew Y. Ng · 2011
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Transfer learning with cluster ensembles
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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Transfer learning with part-based ensembles
Shiliang Sun, Zhijie Xu, and Mo Yang · 2013
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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Kaggle diabetic retinopathy detection, 2015
Kaggle and EyePacs · 2015
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Why M heads are better than one: Training a diverse ensemble of deep networks
Stefan Lee, Senthil Purushwalkam, M. Cogswell, David J. Crandall, and Dhruv Batra · 2015
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Fei-Fei Li, C Lawrence Zitnick, and Ross Girshick · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
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EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2019
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Big transfer (BiT): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2019
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Esteban Real, Jonathon Shlens, Stefano Mazzocchi, Xin Pan, and Vincent Vanhoucke · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
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Automatic Frankensteining: Creating complex ensembles autonomously
Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme · 2017
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TensorFlow Hub , 2018
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Domain adaptive transfer learning with specialist models
Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan, Simon Kornblith, Quoc V. Le, and Ruoming Pang · 2018
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A survey on deep transfer learning
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Rotation equivariant CNNs for digital pathology
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Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
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Do ImageNet classifiers generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Do image classifiers generalize across time?
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To ensemble or not ensemble: When does end-to-end training fail?
Andrew M. Webb, Charles Reynolds, Wenlin Chen, Henry Reeve, Dan-Andrei Iliescu, Mikel Lujan, and Gavin Brown · 2019
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A large-scale study of representation learning with the visual task adaptation benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, and Neil Houlsby · 2019
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A baseline for few-shot image classification
Guneet S. Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2020
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On robustness and transferability of convolutional neural networks
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D’Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, and Mario Lucic · 2020
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The many faces of robustness: A critical analysis of out-of-distribution generalization
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What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang · 2020
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Scalable transfer learning with expert models
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Diverse ensembles improve calibration
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Batchensemble: An alternative approach to efficient ensemble and lifelong learning
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Hyperparameter ensembles for robustness and uncertainty quantification
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