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In machine learning, incorporating more data is often seen as a reliable strategy for improving model performance; this work challenges that notion by demonstrating that the addition of external datasets in many cases can hurt the resulting model's performance.
Assessing the generalizability of prognostic information
Amy C Justice, Kenneth E Covinsky, and Jesse A Berlin · 1999
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What do we mean by validating a prognostic model?
Douglas G Altman and Patrick Royston · 2000
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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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Domain adaptation for visual applications: A comprehensive survey
Gabriela Csurka · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
Earlier work this paper cites.
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study
John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, and Eric Karl Oermann · 2018
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Deep learning predicts hip fracture using confounding patient and healthcare variables
Marcus A Badgeley, John R Zech, Luke Oakden-Rayner, Benjamin S Glicksberg, Manway Liu, William Gale, Michael V McConnell, Bethany Percha, Thomas M Snyder, and Joel T Dudley · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al · 2019
Cited alongside, same era.
Mimic-cxr-jpg, a large publicly available database of labeled chest radiographs
Alistair EW Johnson, Tom J Pollard, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Yifan Peng, Zhiyong Lu, Roger G Mark, Seth J Berkowitz, and Steven Horng · 2019
Cited alongside, same era.
Eduardo HP Pooch, Pedro L Ballester, and Rodrigo C Barros · 2019
Cited alongside, same era.
Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
Cited alongside, same era.
Comparing different deep learning architectures for classification of chest radiographs
From development to deployment: dataset shift, causality, and shift-stable models in health ai
Adarsh Subbaswamy and Suchi Saria · 2020
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Deepsleepnet-lite: A simplified automatic sleep stage scoring model with uncertainty estimates
Luigi Fiorillo, Paolo Favaro, and Francesca Dalia Faraci · 2021
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An empirical framework for domain generalization in clinical settings
Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, and Marzyeh Ghassemi · 2021
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Representation learning with information theory for covid-19 detection
Abel Diaz Berenguer, Tanmoy Mukherjee, Matias Bossa, Nikos Deligiannis, and Hichem Sahli · 2022
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Undersampling is a minimax optimal robustness intervention in nonparametric classification
Niladri S Chatterji, Saminul Haque, and Tatsunori Hashimoto · 2022
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Keno K Bressem, Lisa C Adams, Christoph Erxleben, Bernd Hamm, Stefan M Niehues, and Janis L Vahldiek · 2020
Cited alongside, same era.
Padchest: A large chest x-ray image dataset with multi-label annotated reports
Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, and Maria de la Iglesia-Vayá · 2020
Cited alongside, same era.
On the limits of cross-domain generalization in automated x-ray prediction
Joseph Paul Cohen, Mohammad Hashir, Rupert Brooks, and Hadrien Bertrand · 2020
Cited alongside, same era.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, and Felix Wichmann · 2020
Cited alongside, same era.
An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang · 2020
Cited alongside, same era.
Few-shot adversarial domain adaptation
Saeid Motiian, Quinn Jones, Seyed Iranmanesh, and Gianfranco Doretto
Cited in the paper.
Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto
Cited in the paper.
Later among the works it cites.
Simple data balancing achieves competitive worst-group-accuracy
Badr Youbi Idrissi, Martin Arjovsky, Mohammad Pezeshki, and David Lopez-Paz · 2022
Later among the works it cites.
Out-of-distribution generalization in the presence of nuisance-induced spurious correlations
Aahlad Manas Puli, Lily H Zhang, Eric Karl Oermann, and Rajesh Ranganath · 2022
Later among the works it cites.
Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2023
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