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The topic of generalizing machine learning models learned on a collection of source domains to unknown target domains is challenging.
The fast fourier transform
Henri J. Nussbaumer · 1982
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A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase
Lisa Piotrowski and Fergus William Campbell · 1982
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando C Pereira, and Jennifer Wortman Vaughan · 2009
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, E. Ustinova, Hana Ajakan, Pascal Germain, H. Larochelle, François Laviolette, Mario Marchand, and Victor S. Lempitsky · 2016
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
Earlier work this paper cites.
Learning to generalize: Meta-learning for domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales · 2018
Cited alongside, same era.
Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex Chichung Kot · 2018
Cited alongside, same era.
Generalizing to unseen domains via distribution matching
Isabela Albuquerque, João Monteiro, Mohammad Javad Darvishi Bayazi, Tiago H. Falk, and Ioannis Mitliagkas · 2019
Cited alongside, same era.
Domain generalization via model-agnostic learning of semantic features
Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, and Ben Glocker · 2019
Cited alongside, same era.
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix Wichmann, and Wieland Brendel · 2019
Cited alongside, same era.
Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation
Ling Zhang, Xiaosong Wang, and et al · 2020
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Deep domain-adversarial image generation for domain generalisation
Kaiyang Zhou, Yongxin Yang, Timothy M. Hospedales, and Tao Xiang · 2020
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Domain generalization needs stochastic weight averaging for robustness on domain shifts
Junbum Cha, Han-Cheol Cho, Kyungjae Lee, Seunghyun Park, Yunsung Lee, and Sungrae Park · 2021
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Domain adversarial neural networks for domain generalization: When it works and how to improve
Anthony Sicilia, Xingchen Zhao, and Seong Jae Hwang · 2021
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A fourier-based framework for domain generalization
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Standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge
Hugo J. Kuijf, Adrià Casamitjana, D. Louis Collins, M. Dadar, Achilleas Georgiou, and et al · 2019
Cited alongside, same era.
Self-challenging improves cross-domain generalization
Zeyi Huang, Haohan Wang, Eric P. Xing, and Dong Huang · 2020
Cited alongside, same era.
Domain adaptive medical image segmentation via adversarial learning of disease-specific spatial patterns
Hongwei Li, Timo Loehr, and et al · 2020
Cited alongside, same era.
Domain generalization using a mixture of multiple latent domains
Toshihiko Matsuura and Tatsuya Harada · 2020
Cited alongside, same era.
Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian · 2021
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More is better: A novel multi-view framework for domain generalization
Jian Zhang, Lei Qi, Yinghuan Shi, and Yang Gao · 2021
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Robust white matter hyperintensity segmentation on unseen domain
Xingchen Zhao, Anthony Sicilia, and et al · 2021
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Domain generalization with mixstyle
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang · 2021
Later among the works it cites.