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The goal of domain generalization algorithms is to predict well on distributions different from those seen during training.
The mnist database of handwritten digits
Yann LeCun · 1998
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Statistical learning theory wiley
Vladimir Vapnik · 1998
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Generalizing from several related classification tasks to a new unlabeled sample
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2011
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Unbiased look at dataset bias
Antonio Torralba and Alexei Efros · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Undoing the damage of dataset bias
Aditya Khosla, Tinghui Zhou, Tomasz Malisiewicz, Alexei A Efros, and Antonio Torralba · 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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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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The reusable holdout: Preserving validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth · 2015
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Domain generalization for object recognition with multi-task autoencoders
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Visual domain adaptation: A survey of recent advances
Vishal M Patel, Raghuraman Gopalan, Ruonan Li, and Rama Chellappa · 2015
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ImageNET large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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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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Scatter component analysis: A unified framework for domain adaptation and domain generalization
Muhammad Ghifary, David Balduzzi, W Bastiaan Kleijn, and Mengjie Zhang · 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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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
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Deep CORAL: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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Domain generalization by marginal transfer learning
Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Scott · 2017
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Deep domain generalization with structured low-rank constraint
Zhengming Ding and Yun Fu · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales · 2017
Cited alongside, same era.
Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto · 2017
Cited alongside, same era.
Kernel mean embedding of distributions: A review and beyond
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Bernhard Schölkopf, et al · 2017
A generalization error bound for multi-class domain generalization
Aniket Anand Deshmukh, Yunwen Lei, Srinagesh Sharma, Urun Dogan, James W Cutler, and Clayton Scott · 2019
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Domain generalization via model-agnostic learning of semantic features
Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, and Ben Glocker · 2019
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Domain generalization via multidomain discriminant analysis
Shoubo Hu, Kun Zhang, Zhitang Chen, and Laiwan Chan · 2019
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DIVA: Domain invariant variational autoencoders
Maximilian Ilse, Jakub M Tomczak, Christos Louizos, and Max Welling · 2019
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Support and invertibility in domain-invariant representations
Fredrik D Johansson, David Sontag, and Rajesh Ranganath · 2019
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Cited alongside, same era.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Cited alongside, same era.
Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing
Ehab A AlBadawy, Ashirbani Saha, and Maciej A Mazurowski · 2018
Cited alongside, same era.
MetaReg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
Cited alongside, same era.
Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
Cited alongside, same era.
Dark model adaptation: Semantic image segmentation from daytime to nighttime
Dengxin Dai and Luc Van Gool · 2018
Cited alongside, same era.
Domain generalization with domain-specific aggregation modules
Antonio D’Innocente and Barbara Caputo · 2018
Cited alongside, same era.
Toshihiko Matsuura and Tatsuya Harada · 2019
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Reducing domain gap via style-agnostic networks
Hyeonseob Nam, HyunJae Lee, Jongchan Park, Wonjun Yoon, and Donggeun Yoo · 2019
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PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 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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Unsupervised domain adaptation for medical imaging segmentation with self-ensembling
Christian S Perone, Pedro Ballester, Rodrigo C Barros, and Julien Cohen-Adad · 2019
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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Towards robust cnn-based object detection through augmentation with synthetic rain variations
Georg Volk, Stefan Müller, Alexander von Bernuth, Dennis Hospach, and Oliver Bringmann · 2019
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Learning robust representations by projecting superficial statistics out
Haohan Wang, Zexue He, Zachary C Lipton, and Eric P Xing · 2019
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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 · 2019
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When unseen domain generalization is unnecessary? rethinking data augmentation
Ling Zhang, Xiaosong Wang, Dong Yang, Thomas Sanford, Stephanie Harmon, Baris Turkbey, Holger Roth, Andriy Myronenko, Daguang Xu, and Ziyue Xu · 2019
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On learning invariant representation for domain adaptation
Han Zhao, Remi Tachet des Combes, Kun Zhang, and Geoffrey J Gordon · 2019
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Invariant risk minimization games
Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, and Amit Dhurandhar · 2020
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Improving out-of-distribution generalization via multi-task self-supervised pretraining
Isabela Albuquerque, Nikhil Naik, Junnan Li, Nitish Keskar, and Richard Socher · 2020
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Google’s medical AI was super accurate in a lab. real life was a different story
Will D. Heaven · 2020
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Out-of-distribution generalization via risk extrapolation (REx)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Remi Le Priol, and Aaron Courville · 2020
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Sequential learning for domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales · 2020
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Unshuffling data for improved generalization
Damien Teney, Ehsan Abbasnejad, and Anton van den Hengel · 2020
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Heterogeneous domain generalization via domain mixup
Yufei Wang, Haoliang Li, and Alex C Kot · 2020
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Improve unsupervised domain adaptation with mixup training
Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, and Liu Ren · 2020
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Deep domain-adversarial image generation for domain generalisation
Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, and Tao Xiang · 2020
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