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While deep neural networks can attain good accuracy on in-distribution test points, many applications require robustness even in the face of unexpected perturbations in the input, changes in the domain, or other sources of distribution shift.
Improving predictive inference under covariate shift by weighting the log-likelihood function
H. Shimodaira · 2000
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Y. Grandvalet and Y. Bengio · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Dataset Shift in Machine Learning
J. Quiñonero Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence · 2009
Earlier work this paper cites.
Generalizing from several related classification tasks to a new unlabeled sample
G. Blanchard, G. Lee, and C. Scott · 2011
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
Earlier work this paper cites.
Robust solutions of optimization problems affected by uncertain probabilities
A. Ben-Tal, D. den Hertog, A. De Waegenaere, B. Melenberg, and G. Rennen · 2013
Earlier work this paper cites.
Domain generalization via invariant feature representation
K. Muandet, D. Balduzzi, and B. Schölkopf · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. Berg, and F. Li · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Domain adaptation for visual applications: A comprehensive survey
G. Csurka · 2017
Earlier work this paper cites.
On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Weinberger · 2017
Earlier work this paper cites.
Revisiting batch normalization for practical domain adaptation
Y. Li, N. Wang, J. Shi, J. Liu, and X. Hou · 2017
Earlier work this paper cites.
VisDA: The visual domain adaptation challenge
X. Peng, B. Usman, N. Kaushik, J. Hoffman, D. Wang, and K. Saenko · 2017
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
Earlier work this paper cites.
Does distributionally robust supervised learning give robust classifiers?
W. Hu, G. Niu, I. Sato, and M. Sugiyama · 2018
Earlier work this paper cites.
Exploring the limits of weakly supervised pretraining
D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten · 2018
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Do CIFAR-10 classifiers generalize to CIFAR-10?
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2018
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Group normalization
Y. Wu and K. He · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2019
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Test-time unsupervised domain adaptation
T. Varsavsky, M. Orbes-Arteaga, C. Sudre, M. Graham, P. Nachev, and M. Cardoso · 2020
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A survey of unsupervised deep domain adaptation
G. Wilson and D. Cook · 2020
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Fast is better than free: Revisiting adversarial training
E. Wong, L. Rice, and J. Kolter · 2020
Later among the works it cites.
Tailoring: Encoding inductive biases by optimizing unsupervised objectives at prediction time
F. Alet, M. Bauza, K. Kawaguchi, N. Kuru, T. Lozano-Pérez, and L. Kaelbling · 2021
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby · 2021
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A Fourier perspective on model robustness in computer vision
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
A. Ashukha, A. Lyzhov, D. Molchanov, and D. Vetrov · 2020
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AugMix: A simple data processing method to improve robustness and uncertainty
D. Hendrycks, N. Mu, E. Cubuk, B. Zoph, J. Gilmer, and B. Lakshminarayanan · 2020
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On feature normalization and data augmentation
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Adversarial attacks are reversible with natural supervision
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Tent: Fully test-time adaptation by entropy minimization
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