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Recent empirical studies on domain generalization (DG) have shown that DG algorithms that perform well on some distribution shifts fail on others, and no state-of-the-art DG algorithm performs consistently well on all shifts.
Invariant risk minimization, 2019
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 1907
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Learning methods for generic object recognition with invariance to pose and lighting
Y. LeCun, Fu Jie Huang, and L. Bottou · 2004
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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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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Domain generalization for object recognition with multi-task autoencoders
M. Ghifary, W. Kleijn, M. Zhang, and D. Balduzzi · 2015
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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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Scatter component analysis: A unified framework for domain adaptation and domain generalization
Muhammad Ghifary, David Balduzzi, W Bastiaan Kleijn, and Mengjie Zhang · 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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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei Efros · 2017
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From detection of individual metastases to classification of lymph node status at the patient level: The camelyon17 challenge
Peter Bandi, Oscar Geessink, Quirine Manson, Marcory van Dijk, Maschenka Balkenhol, Meyke Hermsen, Babak Ehteshami Bejnordi, Byungjae Lee, Kyunghyun Paeng, Aoxiao Zhong, Quanzheng Li, Farhad Ghazvinian Zanjani, Sveta Zinger, Keisuke Fukuta, Daisuke Komura, Vlado Ovtcharov, Shenghua Cheng, Shaoqun Zeng, Jeppe Thagaard, and Geert Litjens · 2018
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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Measuring and mitigating unintended bias in text classification
Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2018
Cited alongside, same era.
Machine learning methods for histopathological image analysis
Daisuke Komura and Shumpei Ishikawa · 2018
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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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Reducing gender bias in abusive language detection
Ji Ho Park, Jamin Shin, and Pascale Fung · 2018
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Nuanced metrics for measuring unintended bias with real data for text classification
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2019
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Domain generalization via multidomain discriminant analysis
Shoubo Hu, Kun Zhang, Zhitang Chen, and Laiwan Chan · 2019
Invariance principle meets information bottleneck for out-of-distribution generalization
Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish · 2021
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard L. Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
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Out-of-distribution generalization via risk extrapolation (rex)
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 using causal matching
Divyat Mahajan, Shruti Tople, and Amit Sharma · 2021
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Cited alongside, same era.
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
David Tellez, Geert Litjens, Péter Bándi, Wouter Bulten, John-Melle Bokhorst, Francesco Ciompi, and Jeroen van der Laak · 2019
Cited alongside, same era.
Generalizing to unseen domains via distribution matching, 2020
Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk, and Ioannis Mitliagkas · 2020
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, and Quoc Le · 2020
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.
Weakly-supervised disentanglement without compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen · 2020
Cited alongside, same era.
Efficient domain generalization via common-specific low-rank decomposition
Vihari Piratla, Praneeth Netrapalli, and Sunita Sarawagi · 2020
Cited alongside, same era.
Reducing domain gap by reducing style bias
Hyeonseob Nam, Hyunjae Lee, Jongchan Park, Wonjun Yoon, and Donggeun Yoo · 2021
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Toward causal representation learning
B. Schölkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio · 2021
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Counterfactual invariance to spurious correlations in text classification
Victor Veitch, Alexander D’Amour, Steve Yadlowsky, and Jacob Eisenstein · 2021
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Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng, and Tao Qin · 2021
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Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2021
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Invariance principle meets out-of-distribution generalization on graphs
Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, and James Cheng · 2022
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Causally motivated shortcut removal using auxiliary labels
Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, and Alexander D’Amour · 2022
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A fine-grained analysis on distribution shift
Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dj Dvijotham, and Ali Taylan Cemgil · 2022
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Improving out-of-distribution robustness via selective augmentation
Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn · 2022
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Ood-bench: Quantifying and understanding two dimensions of out-of-distribution generalization
Nanyang Ye, Kaican Li, Haoyue Bai, Runpeng Yu, Lanqing Hong, Fengwei Zhou, Zhenguo Li, and Jun Zhu · 2022
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