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Machine learning models fail to perform when facing out-of-distribution (OOD) domains, a challenging task known as domain generalization (DG).
Flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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The tradeoffs of large scale learning
Léon Bottou and Olivier Bousquet · 2007
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Mark J Schervish · 2012
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N Rockmore · 2013
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Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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On the saddle point problem for non-convex optimization
Razvan Pascanu, Yann N Dauphin, Surya Ganguli, and Yoshua Bengio · 2014
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Empirical analysis of the hessian of over-parametrized neural networks
Levent Sagun, Utku Evci, V Ugur Guney, Yann Dauphin, and Leon Bottou · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Block-normalized gradient method: An empirical study for training deep neural network
Adams Wei Yu, Lei Huang, Qihang Lin, Ruslan Salakhutdinov, and Jaime Carbonell · 2017
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Central moment discrepancy (cmd) for domain-invariant representation learning
Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Gradient descent happens in a tiny subspace
Guy Gur-Ari, Daniel A Roberts, and Ethan Dyer · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese · 2018
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Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2019
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Efficient domain generalization via common-specific low-rank decomposition
Vihari Piratla, Praneeth Netrapalli, and Sunita Sarawagi · 2020
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Invariance principle meets information bottleneck for out-of-distribution generalization
Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish · 2021
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
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Does invariant risk minimization capture invariance?
Pritish Kamath, Akilesh Tangella, Danica Sutherland, and Nathan Srebro · 2021
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Selfreg: Self-supervised contrastive regularization for domain generalization
Daehee Kim, Youngjun Yoo, Seunghyun Park, Jinkyu Kim, and Jaekoo Lee · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Domain generalization via model-agnostic learning of semantic features
Qi Dou, Daniel C. Castro, Konstantinos Kamnitsas, and Ben Glocker · 2019
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An investigation into neural net optimization via hessian eigenvalue density
Behrooz Ghorbani, Shankar Krishnan, and Ying Xiao · 2019
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Episodic training for domain generalization
Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, and Timothy M Hospedales · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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How noise affects the hessian spectrum in overparameterized neural networks
Mingwei Wei and David J Schwab · 2019
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Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
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Learning invariant representations and risks for semi-supervised domain adaptation
Bo Li, Yezhen Wang, Shanghang Zhang, Dongsheng Li, Kurt Keutzer, Trevor Darrell, and Han Zhao · 2021
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Domain generalization via gradient surgery
Lucas Mansilla, Rodrigo Echeveste, Diego H Milone, and Enzo Ferrante · 2021
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Fishr: Invariant gradient variances for out-of-distribution generalization
Alexandre Rame, Corentin Dancette, and Matthieu Cord · 2021
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Soroosh Shahtalebi, Jean-Christophe Gagnon-Audet, Touraj Laleh, Mojtaba Faramarzi, Kartik Ahuja, and Irina Rish · 2021
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Gradient matching for domain generalization
Yuge Shi, Jeffrey Seely, Philip H. S. Torr, N. Siddharth, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve · 2021
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Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng, and Tao Qin · 2021
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A fourier-based framework for domain generalization
Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian · 2021
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Adaptive risk minimization: Learning to adapt to domain shift
M. Zhang, H. Marklund, N. Dhawan, A. Gupta, S. Levine, and C. Finn · 2021
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Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2021
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