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Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization.
Image Alignment in Unseen Domains via Domain Deep Generalization
Truong, T.; Luu, K.; Duong, C. N.; Le, N.; and Tran, M. 2019 · 1905
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Arjovsky, M.; Bottou, L.; Gulrajani, I.; and Lopez-Paz, D. 2019 · 1907
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Reducing Domain Gap by Reducing Style Bias
Nam, H.; Lee, H.; Park, J.; Yoon, W.; and Yoo, D. 2021 · 1910
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Sagawa, S.; Koh, P. W.; Hashimoto, T. B.; and Liang, P. 2019 · 1911
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An overview of statistical learning theory
Vapnik, V. 1999 · 1999
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Improve Unsupervised Domain Adaptation with Mixup Training
Yan, S.; Song, H.; Li, N.; Zou, L.; and Ren, L. 2020 · 2001
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Multi-source domain adaptation in the deep learning era: A systematic survey
Zhao, S.; Li, B.; Xu, P.; and Keutzer, K. 2020b · 2002
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Out-of-Distribution Generalization via Risk Extrapolation (REx)
Krueger, D.; Caballero, E.; Jacobsen, J.; Zhang, A.; Binas, J.; Priol, R. L.; and Courville, A. C. 2020a · 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. 2020b · 2003
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Rethinking distributional matching based domain adaptation
Li, B.; Wang, Y.; Che, T.; Zhang, S.; Zhao, S.; Xu, P.; Zhou, W.; Bengio, Y.; and Keutzer, K. 2020a · 2006
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Domain Generalization using Causal Matching
Mahajan, D.; Tople, S.; and Sharma, A. 2020 · 2006
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In Search of Lost Domain Generalization
Gulrajani, I.; and Lopez-Paz, D. 2020 · 2007
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Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
Zhang, M.; Marklund, H.; Gupta, A.; Levine, S.; and Finn, C. 2020 · 2007
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Causal Inference
Pearl, J. 2010 · 2008
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Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
Li, B.; Wang, Y.; Zhang, S.; Li, D.; Darrell, T.; Keutzer, K.; and Zhao, H. 2020b · 2010
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The Risks of Invariant Risk Minimization
Rosenfeld, E.; Ravikumar, P.; and Risteski, A. 2020 · 2010
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Batch Normalization Embeddings for Deep Domain Generalization
Segù, M.; Tonioni, A.; and Tombari, F. 2020 · 2011
Cited alongside, same era.
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Zhao, H.; Dan, C.; Aragam, B.; Jaakkola, T. S.; Gordon, G. J.; and Ravikumar, P. 2020a · 2012
Cited alongside, same era.
Domain Generalization via Invariant Feature Representation
Muandet, K.; Balduzzi, D.; and Schölkopf, B. 2013 · 2013
Cited alongside, same era.
Learning transferable features with deep adaptation networks
Long, M.; Cao, Y.; Wang, J.; and Jordan, M. I. 2015 · 2015
Cited alongside, same era.
A Minimax Approach to Supervised Learning
Farnia, F.; and Tse, D. 2016 · 2016
Cited alongside, same era.
Domain Agnostic Learning with Disentangled Representations
Peng, X.; Huang, Z.; Sun, X.; and Saenko, K. 2019 · 2019
Later among the works it cites.
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
Riemer, M.; Cases, I.; Ajemian, R.; Liu, M.; Rish, I.; Tu, Y.; and Tesauro, G. 2019 · 2019
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Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data
Yue, X.; Zhang, Y.; Zhao, S.; Sangiovanni-Vincentelli, A. L.; Keutzer, K.; and Gong, B. 2019 · 2019
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DeceptionNet: Network-Driven Domain Randomization
Zakharov, S.; Kehl, W.; and Ilic, S. 2019 · 2019
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Invariant Rationalization
Chang, S.; Zhang, Y.; Yu, M.; and Jaakkola, T. S. 2020 · 2020
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Learning to Learn with Variational Information Bottleneck for Domain Generalization
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Identity Mappings in Deep Residual Networks
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Deep Variational Information Bottleneck
Alemi, A. A.; Fischer, I.; Dillon, J. V.; and Murphy, K. 2017 · 2017
Cited alongside, same era.
Domain-Adversarial Training of Neural Networks
Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. S. 2017 · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
Tzeng, E.; Hoffman, J.; Saenko, K.; and Darrell, T. 2017 · 2017
Cited alongside, same era.
Metareg: Towards domain generalization using meta-regularization
Balaji, Y.; Sankaranarayanan, S.; and Chellappa, R. 2018 · 2018
Cited alongside, same era.
Applying Domain Randomization to Synthetic Data for Object Category Detection
Borrego, J.; Dehban, A.; Figueiredo, R.; Moreno, P.; Bernardino, A.; and Santos-Victor, J. 2018 · 2018
Cited alongside, same era.
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
Liu, A. H.; Liu, Y.; Yeh, Y.; and Wang, Y. F. 2018 · 2018
Cited alongside, same era.
Du, Y.; Xu, J.; Xiong, H.; Qiu, Q.; Zhen, X.; Snoek, C. G. M.; and Shao, L. 2020 · 2020
Later among the works it cites.
Self-challenging Improves Cross-Domain Generalization
Huang, Z.; Wang, H.; Xing, E. P.; and Huang, D. 2020 · 2020
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Domain Generalization Using Shape Representation
Nazari, N. H.; and Kovashka, A. 2020 · 2020
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Learning to Learn Single Domain Generalization
Qiao, F.; Zhao, L.; and Peng, X. 2020 · 2020
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Domain adaptation with conditional distribution matching and generalized label shift
Tachet des Combes, R.; Zhao, H.; Wang, Y.-X.; and Gordon, G. J. 2020 · 2020
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Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
Ahuja, K.; Caballero, E.; Zhang, D.; Bengio, Y.; Mitliagkas, I.; and Rish, I. 2021 · 2021
Closest in time.
Domain Generalization by Marginal Transfer Learning
Blanchard, G.; Deshmukh, A. A.; Dogan, Ü.; Lee, G.; and Scott, C. 2021 · 2021
Closest in time.
Understanding the failure modes of out-of-distribution generalization
Nagarajan, V.; Andreassen, A.; and Neyshabur, B. 2021 · 2021
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MADAN: multi-source adversarial domain aggregation network for domain adaptation
Zhao, S.; Li, B.; Xu, P.; Yue, X.; Ding, G.; and Keutzer, K. 2021 · 2021
Closest in time.
Domain-adversarial training of neural networks
Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016 · 2030
Closest in time.