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Domain generalization (DG) aims at generalizing a classifier trained on multiple source domains to an unseen target domain with domain shift.
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Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2011
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Von mises-fisher clustering models
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Fully convolutional networks for semantic segmentation
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 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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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Learning to generalize: Meta-learning for domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2018
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Self-challenging improves cross-domain generalization
Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang · 2020
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2020
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Heterogeneous domain generalization via domain mixup
Yufei Wang, Haoliang Li, and Alex C Kot · 2020
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Adaptive risk minimization: A meta-learning approach for tackling group distribution shift
Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, and Chelsea Finn · 2020
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Domain generalization by marginal transfer learning
Gilles Blanchard, Aniket Anand Deshmukh, Ürün Dogan, Gyemin Lee, and Clayton Scott · 2021
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Swad: Domain generalization by seeking flat minima
Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park · 2021
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
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Domain generalization via conditional invariant representations
Ya Li, Mingming Gong, Xinmei Tian, Tongliang Liu, and Dacheng Tao · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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On learning invariant representations for domain adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon · 2019
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Invariant risk minimization
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2020
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
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Test-time classifier adjustment module for model-agnostic domain generalization
Yusuke Iwasawa and Yutaka Matsuo · 2021
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Out-of-distribution generalization via risk extrapolation
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
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Domain generalization under conditional and label shifts via variational bayesian inference
Xiaofeng Liu, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges EL Fakhri, and Jonghye Woo · 2021
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Magface: A universal representation for face recognition and quality assessment
Qiang Meng, Shichao Zhao, Zhida Huang, and Feng Zhou · 2021
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Reducing domain gap by reducing style bias
Hyeonseob Nam, HyunJae Lee, Jongchan Park, Wonjun Yoon, and Donggeun Yoo · 2021
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