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The robustness of machine learning algorithms to distributions shift is primarily discussed in the context of supervised learning (SL).
Learning internal representations by error propagation
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Importance weighted autoencoders
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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Sgd on neural networks learns functions of increasing complexity
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary C. Lipton, and Eric P. Xing · 2019
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Preventing dataset shift from breaking machine-learning biomarkers
Jérôme Dockès, Gaël Varoquaux, and Jean-Baptiste Poline · 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 Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran Haque, Sara M. Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
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Out-of-distribution generalization with maximal invariant predictor
Masanori Koyama and Shoichiro Yamaguchi · 2021
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Self-supervised learning is more robust to dataset imbalance
Hong Liu, Jeff Z HaoChen, Adrien Gaidon, and Tengyu Ma · 2021
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Rectifying the shortcut learning of background for few-shot learning
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