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In Domain Generalization (DG) settings, models trained independently on a given set of training domains have notoriously chaotic performance on distribution shifted test domains, and stochasticity in optimization (e.g.
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Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Adam: A method for stochastic optimization
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Domain-adversarial training of neural networks
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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
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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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Deep hashing network for unsupervised domain adaptation
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification
Prateek Jain, Sham Kakade, Rahul Kidambi, Praneeth Netrapalli, and Aaron Sidford · 2018
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Learning to generalize: Meta-learning for domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
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Self-challenging improves cross-domain generalization
Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang · 2020
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Heterogeneous domain generalization via domain mixup
Yufei Wang, Haoliang Li, and Alex C Kot · 2020
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Adversarial domain adaptation with domain mixup
Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang · 2020
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Deep domain generalization via conditional invariant adversarial networks
Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, and Dacheng Tao · 2018
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Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens Van Der Maaten · 2018
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Iterate averaging as regularization for stochastic gradient descent
Gergely Neu and Lorenzo Rosasco · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 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
Cited alongside, same era.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Cited alongside, same era.
Rethinking bias-variance trade-off for generalization of neural networks
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, and Yi Ma · 2020
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Self-supervised learning across domains
Silvia Bucci, Antonio D’Innocente, Yujun Liao, Fabio Maria Carlucci, Barbara Caputo, and Tatiana Tommasi · 2021
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Swad: Domain generalization by seeking flat minima
Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, 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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Fishr: Invariant gradient variances for out-of-distribution generalization
Alexandre Rame, Corentin Dancette, and Matthieu Cord · 2021
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Gradient matching for domain generalization
Yuge Shi, Jeffrey Seely, Philip HS Torr, N Siddharth, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve · 2021
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An empirical investigation of domain generalization with empirical risk minimizers
Ramakrishna Vedantam, David Lopez-Paz, and David J Schwab · 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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Semi-supervised domain generalization with stochastic stylematch
Kaiyang Zhou, Chen Change Loy, and Ziwei Liu · 2021
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Domain generalization by mutual-information regularization with pre-trained models
Junbum Cha, Kyungjae Lee, Sungrae Park, and Sanghyuk Chun · 2022
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Revisiting weakly supervised pre-training of visual perception models
Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Bugra Gedik, Raj Prateek Kosaraju, Dhruv Mahajan, Ross Girshick, Piotr Dollár, and Laurens van der Maaten · 2022
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