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Though remarkable progress has been achieved in various vision tasks, deep neural networks still suffer obvious performance degradation when tested in out-of-distribution scenarios.
Principles of risk minimization for learning theory
V. Vapnik · 1992
Earlier work this paper cites.
An overview of statistical learning theory
Vladimir N Vapnik · 1999
Earlier work this paper cites.
Domain adaptive ensemble learning
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang · 2003
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
Earlier work this paper cites.
Generalizing from several related classification tasks to a new unlabeled sample
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2011
Earlier work this paper cites.
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Ergys Ristani, Francesco Solera, Roger S. Zou, R. Cucchiara, and Carlo Tomasi · 2016
Earlier work this paper cites.
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On feature normalization and data augmentation
Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, and Kilian Q Weinberger · 2021
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