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Sample selection bias as a specification error (with an application to the estimation of labor supply functions), 1977
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Imagenet: A large-scale hierarchical image database
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Covariate shift by kernel mean matching
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Domain adaptation: Learning bounds and algorithms
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When training and test sets are different: characterizing learning transfer
A. Storkey · 2009
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On causal and anticausal learning
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Domain generalization via invariant feature representation
K. Muandet, D. Balduzzi, and B. Schölkopf · 2013
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Domain adaptation under target and conditional shift
K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang · 2013
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Domain generalization for object recognition with multi-task autoencoders
M. Ghifary, W. Bastiaan Kleijn, M. Zhang, and D. Balduzzi · 2015
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Multi-view domain generalization for visual recognition
L. Niu, W. Li, and D. Xu · 2015
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Domain separation networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
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Robust domain generalisation by enforcing distribution invariance
S. Erfani, M. Baktashmotlagh, M. Moshtaghi, V. Nguyen, C. Leckie, J. Bailey, and R. Kotagiri · 2016
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Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
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Does distributionally robust supervised learning give robust classifiers?
W. Hu, G. Nio, I. Sato, and M. Sugiyama · 2016
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Unsupervised domain adaptation with residual transfer networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2016
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The sketchy database: Learning to retrieve badly drawn bunnies
P. Sangkloy, N. Burnell, C. Ham, and J. Hays · 2016
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Select-additive learning: Improving generalization in multimodal sentiment analysis
H. Wang, A. Meghawat, L.-P. Morency, and E. P. Xing · 2016
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A survey of transfer learning
K. Weiss, T. M. Khoshgoftaar, and D. Wang · 2016
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Unsupervised pixel-level domain adaptation with generative adversarial networks
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2017
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Domain adaptation for visual applications: A comprehensive survey
G. Csurka · 2017
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Co-regularized alignment for unsupervised domain adaptation
A. Kumar, P. Sattigeri, K. Wadhawan, L. Karlinsky, R. Feris, B. Freeman, and G. Wornell · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K. Lee, K. Lee, H. Lee, and J. Shin · 2018
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Detecting and correcting for label shift with black box predictors
Z. C. Lipton, Y.-X. Wang, and A. Smola · 2018
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Conditional adversarial domain adaptation
M. Long, Z. CAO, J. Wang, and M. I. Jordan · 2018
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Best sources forward: domain generalization through source-specific nets
M. Mancini, S. R. Bulò, B. Caputo, and E. Ricci · 2018
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Quick draw! the data, 2018
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T. Gebru, J. Hoffman, and L. Fei-Fei · 2017
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Simultaneous deep transfer across domains and tasks
J. Hoffman, E. Tzeng, T. Darrell, and K. Saenko · 2017
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Measuring the tendency of cnns to learn surface statistical regularities
J. Jo and Y. Bengio · 2017
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Deeper, broader and artier domain generalization
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales · 2017
Cited alongside, same era.
Unified deep supervised domain adaptation and generalization
S. Motiian, M. Piccirilli, D. A. Adjeroh, and G. Doretto · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Cited alongside, same era.
Information dropout: Learning optimal representations through noisy computation
A. Achille and S. Soatto · 2018
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QuickDraw · 2018
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Generalizing to unseen domains via adversarial data augmentation
R. Volpi, H. Namkoong, O. Sener, J. C. Duchi, V. Murino, and S. Savarese · 2018
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Deep visual domain adaptation: A survey
M. Wang and W. Deng · 2018
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Learning semantic representations for unsupervised domain adaptation
S. Xie, Z. Zheng, L. Chen, and C. Chen · 2018
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Domain generalization by solving jigsaw puzzles
F. M. Carlucci, A. D’Innocente, S. Bucci, B. Caputo, and T. Tommasi · 2019
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
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Support and invertibility in domain-invariant representations
F. D. Johansson, R. Ranganath, and D. Sontag · 2019
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Do imagenet classifiers generalize to imagenet?, 2019
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2019
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Multi-domain adversarial learning
A. Schoenauer-Sebag, L. Heinrich, M. Schoenauer, M. Sebag, L. Wu, and S. Altschuler · 2019
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Learning robust representations by projecting superficial statistics out
H. Wang, Z. He, Z. C. Lipton, and E. P. Xing · 2019
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Domain adaptation with asymmetrically-relaxed distribution alignment
Y. Wu, E. Winston, D. Kaushik, and Z. Lipton · 2019
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