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A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data distribution is not i.i.d to the training domains.
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Model-based robust deep learning
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Deep coral: Correlation alignment for deep domain adaptation
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Improve unsupervised domain adaptation with mixup training
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Adaptive risk minimization: A meta-learning approach for tackling group distribution shift
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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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Gradient matching for domain generalization
Yuge Shi, Jeffrey Seely, Philip HS Torr, N Siddharth, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve · 2021
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Reducing domain gap by reducing style bias, 2021
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