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Machine learning models trained with purely observational data and the principle of empirical risk minimization \citep{vapnik_principles_1992} can fail to generalize to unseen domains.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Principles of Risk Minimization for Learning Theory
Vapnik, V. 1992 · 1992
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Gradient-Based Learning Applied to Document Recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Ha, P. 1998 · 1998
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Affinity and Diversity: Quantifying Mechanisms of Data Augmentation
Gontijo-Lopes, R.; Smullin, S. J.; Cubuk, E. D.; and Dyer, E. 2020 · 2002
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Cranmer, M.; Greydanus, S.; Hoyer, S.; Battaglia, P.; Spergel, D.; and Ho, S. 2020 · 2003
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Out-of-Distribution Generalization via Risk Extrapolation (REx)
Krueger, D.; Caballero, E.; Jacobsen, J.-H.; Zhang, A.; Binas, J.; Priol, R. L.; and Courville, A. 2020 · 2003
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On the Benefits of Invariance in Neural Networks
Lyle, C.; van der Wilk, M.; Kwiatkowska, M.; Gal, Y.; and Bloem-Reddy, B. 2020 · 2005
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Causal inference in statistics: An overview
Pearl, J. 2009 · 2009
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Undoing the Damage of Dataset Bias
Khosla, A.; Zhou, T.; Malisiewicz, T.; Efros, A. A.; and Torralba, A. 2012 · 2012
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Domain Generalization via Invariant Feature Representation
Muandet, K.; Balduzzi, D.; and Schölkopf, B. 2013 · 2013
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Deep Domain Confusion: Maximizing for Domain Invariance
Tzeng, E.; Hoffman, J.; Zhang, N.; Saenko, K.; and Darrell, T. 2014 · 2014
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Domain Generalization for Object Recognition with Multi-task Autoencoders
Ghifary, M.; Kleijn, W. B.; Zhang, M.; and Balduzzi, D. 2015 · 2015
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Causal inference and the data-fusion problem
Bareinboim, E.; and Pearl, J. 2016 · 2016
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Group Equivariant Convolutional Networks
Cohen, T. S.; and Welling, M. 2016 · 2016
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Domain-Adversarial Training of Neural Networks
Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016 · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Peters, J.; Bühlmann, P.; and Meinshausen, N. 2016 · 2016
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Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2017 · 2017
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Unified Deep Supervised Domain Adaptation and Generalization
Motiian, S.; Piccirilli, M.; Adjeroh, D. A.; and Doretto, G. 2017 · 2017
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The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Perez, L.; and Wang, J. 2017 · 2017
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Elements of Causal Inference: Foundations and Learning Algorithms
Peters, J.; Janzing, D.; and Schölkopf, B. 2017 · 2017
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Invariant Risk Minimization
Arjovsky, M.; Bottou, L.; Gulrajani, I.; and Lopez-Paz, D. 2019 · 2019
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Why do deep convolutional networks generalize so poorly to small image transformations?
Azulay, A.; and Weiss, Y. 2019 · 2019
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Domain Generalization by Solving Jigsaw Puzzles
Carlucci, F. M.; D’Innocente, A.; Bucci, S.; Caputo, B.; and Tommasi, T. 2019 · 2019
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Causality matters in medical imaging
Castro, D. C.; Walker, I.; and Glocker, B. 2019 · 2019
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AutoAugment: Learning Augmentation Strategies From Data
Cubuk, E. D.; Zoph, B.; Mane, D.; Vasudevan, V.; and Le, Q. V. 2019 · 2019
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Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Gowal, S.; Qin, C.; Huang, P.-S.; Cemgil, T.; Dvijotham, K.; Mann, T.; and Kohli, P. 2019 · 2019
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Tobin, J.; Fong, R.; Ray, A.; Schneider, J.; Zaremba, W.; and Abbeel, P. 2017 · 2017
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MetaReg: Towards Domain Generalization using Meta-Regularization
Balaji, Y.; Sankaranarayanan, S.; and Chellappa, R. 2018 · 2018
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Hallucinating Agnostic Images to Generalize Across Domains
Carlucci, F. M.; Russo, P.; Tommasi, T.; and Caputo, B. 2018 · 2018
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Deep Domain Generalization With Structured Low-Rank Constraint
Ding, Z.; and Fu, Y. 2018 · 2018
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Deep Domain Generalization via Conditional Invariant Adversarial Networks
Li, Y.; Tian, X.; Gong, M.; Liu, Y.; Liu, T.; Zhang, K.; and Tao, D. 2018 · 2018
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Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Magliacane, S.; van Ommen, T.; Claassen, T.; Bongers, S.; Versteeg, P.; and Mooij, J. M. 2018 · 2018
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Best sources forward: domain generalization through source-specific nets
Mancini, M.; Bulò, S. R.; Caputo, B.; and Ricci, E. 2018 · 2018
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Conditional Variance Penalties and Domain Shift Robustness
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DIVA: Domain Invariant Variational Autoencoders
Ilse, M.; Tomczak, J. M.; Louizos, C.; and Welling, M. 2019 · 2019
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Support and Invertibility in Domain-Invariant Representations
Johansson, F. D.; Sontag, D.; and Ranganath, R. 2019 · 2019
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Joint Causal Inference from Multiple Contexts
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A survey on image data augmentation for deep learning
Shorten, C.; and Khoshgoftaar, T. M. 2019 · 2019
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From development to deployment: dataset shift, causality, and shift-stable models in health AI
Subbaswamy, A.; and Saria, S. 2019 · 2019
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Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport
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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Tellez, D.; Litjens, G.; Bandi, P.; Bulten, W.; Bokhorst, J.-M.; Ciompi, F.; and van der Laak, J. 2019 · 2019
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On Learning Invariant Representation for Domain Adaptation
Zhao, H.; Combes, R. T. d.; Zhang, K.; and Gordon, G. J. 2019 · 2019
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Automating Data Augmentation: Practice, Theory and New Direction
Li, S. Y. 2020 · 2020
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