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Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training domains.
Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories
Fei-Fei Li, Fergus Rob, and Perona Pietro · 2004
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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Free viewpoint action recognition using motion history volumes
Daniel Weinland, Remi Ronfard, and Edmond Boyer · 2006
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Labelme: A database and web-based tool for image annotation
Bryan C Russell, Antonio Torralba, Kevin P Murphy, and William T Freeman · 2008
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Exploiting hierarchical context on a large database of object categories
Myung Jin Choi, Joseph Lim, and Antonio Torralba · 2010
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew. Zisserman · 2010
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Undoing the damage of dataset bias
Aditya Khosla, Tinghui Zhou, Tomasz Malisiewicz, Alexei 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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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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Exploiting low-rank structure from latent domains for domain generalization
Zheng Xu, Wen Li, Li Niu, and Dong Xu · 2014
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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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
Cited alongside, same era.
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, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
A unified perspective on multi-domain and multi-task learning
Yongxin Yang and Timothy M. Hospedales · 2015
Cited alongside, same era.
Domain separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A. Adjeroh, and Gianfranco Doretto · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
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Prototypical networks for few shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
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Learning to generalize: Meta-learning for domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales · 2018
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Cited alongside, same era.
Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I. Jordan · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Universal representations: The missing link between faces, text, planktons, and cat breeds
Hakan Bilen and Andrea Vedaldi · 2017
Cited alongside, same era.
Entropy-sgd: Biasing gradient descent into wide valleys
Pratik Chaudhar, Anna Choromansk, Stefano Soatt, Yann LeCun, Carlo Baldass, Christian Borg, Jennifer Chays, Levent Sagun, and Riccardo Zecchina · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2017
Cited alongside, same era.
Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C. Kot · 2018
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Best sources forward: Domain generalization through source-specific nets
Massimiliano Mancini, Samuel Rota Bulo‘, Barbara Caputo, and Elisa Ricci · 2018
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Efficient parametrization of multi-domain deep neural networks
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2018
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Generalizing across domains via cross-gradient training
Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, and Sunita Sarawagi · 2018
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Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John Duchi, Vittorio Murino, and Silvio Savarese · 2018
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Deep mutual learning
Ying Zhang, Tao Xiang, Timothy M. Hospedales, and Huchuan Lu · 2018
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Feature-critic networks for heterogeneous domain generalization
Yiying Li, Yongxin Yang, Wei Zhou, and Timothy M. Hospedales · 2019
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