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Domain Generalization (DG) aims to learn models whose performance remains high on unseen domains encountered at test-time by using data from multiple related source domains.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, et al · 2007
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Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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Optimal transport: old and new
Cédric Villani · 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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Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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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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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 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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Statistics of robust optimization: A generalized empirical likelihood approach
John Duchi, Peter Glynn, and Hongseok Namkoong · 2016
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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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Distributionally robust stochastic optimization with wasserstein distance
Rui Gao and Anton J Kleywegt · 2016
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Stochastic optimization for large-scale optimal transport
Aude Genevay, Marco Cuturi, Gabriel Peyré, and Francis Bach · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 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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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, Riccardo Volpi, and John Duchi · 2017
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Data-driven robust optimization
Dimitris Bertsimas, Vishal Gupta, and Nathan Kallus · 2018
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Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation
Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, and Nicolas Courty · 2018
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Learning generative models with sinkhorn divergences
Aude Genevay, Gabriel Peyré, and Marco Cuturi · 2018
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Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
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Benchmarking neural network robustness to common corruptions and surface variations
Dan Hendrycks and Thomas G Dietterich · 2018
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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama · 2018
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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
Cited alongside, same era.
Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
Cited alongside, same era.
Data-driven distributionally robust optimization using the wasserstein metric: Performance guarantees and tractable reformulations
Peyman Mohajerin Esfahani and Daniel Kuhn · 2018
Cited alongside, same era.
Technical report on the cleverhans v2.1.0 adversarial examples library
Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko Roy, Alexander Matyasko, Vahid Behzadan, Karen Hambardzumyan, Zhishuai Zhang, Yi-Lin Juang, Zhi Li, Ryan Sheatsley, Abhibhav Garg, Jonathan Uesato, Willi Gierke, Yinpeng Dong, David Berthelot, Paul Hendricks, Jonas Rauber, and Rujun Long · 2018
Cited alongside, same era.
Semidefinite relaxations for certifying robustness to adversarial examples
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
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Bridging theory and algorithm for domain adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael Jordan · 2019
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On learning invariant representations for domain adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon · 2019
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Geometric dataset distances via optimal transport
David Alvarez-Melis and Nicolo Fusi · 2020
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Anomalous example detection in deep learning: A survey
Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K Varshney, and Dawn Song · 2020
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On the transfer of disentangled representations in realistic settings
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Aditi Raghunathan, Jacob Steinhardt, and Percy S Liang · 2018
Cited alongside, same era.
Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu · 2018
Cited alongside, same era.
Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
Cited alongside, same era.
Data-driven risk-averse stochastic optimization with wasserstein metric
Chaoyue Zhao and Yongpei Guan · 2018
Cited alongside, same era.
Adversarial multiple source domain adaptation
Han Zhao, Shanghang Zhang, Guanhang Wu, José MF Moura, Joao P Costeira, and Geoffrey J Gordon · 2018
Cited alongside, same era.
Generalizing to unseen domains via distribution matching
Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H Falk, and Ioannis Mitliagkas · 2019
Cited alongside, same era.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Cited alongside, same era.
Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf · 2020
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
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The role of disentanglement in generalisation
Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, and Jeffrey Bowers · 2020
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Learning to learn single domain generalization
Fengchun Qiao, Long Zhao, and Xi Peng · 2020
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Macer: Attack-free and scalable robust training via maximizing certified radius
Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, and Liwei Wang · 2020
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Defending against image corruptions through adversarial augmentations
Dan A Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, Andras Gyorgy, Timothy Mann, and Sven Gowal · 2021
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Generalised lipschitz regularisation equals distributional robustness
Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, and Simon Kornblith · 2021
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A winning hand: Compressing deep networks can improve out-of-distribution robustness
James Diffenderfer, Brian Bartoldson, Shreya Chaganti, Jize Zhang, and Bhavya Kailkhura · 2021
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Pot: Python optimal transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer · 2021
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On the effectiveness of adversarial training against common corruptions
Klim Kireev, Maksym Andriushchenko, and Nicolas Flammarion · 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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How robust are randomized smoothing based defenses to data poisoning?
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm · 2021
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Understanding the limits of unsupervised domain adaptation via data poisoning
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm · 2021
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Robust learning meets generative models: Can proxy distributions improve adversarial robustness?
Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, and Prateek Mittal · 2021
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Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, and Z Morley Mao · 2021
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Augmax: Adversarial composition of random augmentations for robust training
Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, and Zhangyang Wang · 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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Quantifying and improving transferability in domain generalization
Guojun Zhang, Han Zhao, Yaoliang Yu, and Pascal Poupart · 2021
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Towards principled disentanglement for domain generalization
Hanlin Zhang, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, and Eric P Xing · 2021
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Certifying model accuracy under distribution shifts
Aounon Kumar, Alexander Levine, Tom Goldstein, and Soheil Feizi · 2022
Closest in time.
Certifying out-of-domain generalization for blackbox functions
Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, and Ce Zhang · 2022
Closest in time.