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Domain generalization aims to enhance the model robustness against domain shift without accessing the target domain.
Cycada: Cycle-consistent adversarial domain adaptation. In International conference on machine learning . PMLR, 1989–1998
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell. 2018 · 1998
Earlier work this paper cites.
Semi-supervised learning by entropy minimization. In Proceedings of the 17th International Conference on Neural Information Processing Systems . 529–536
Yves Grandvalet and Yoshua Bengio. 2004 · 2004
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, et al · 2007
Earlier work this paper cites.
Perception of object shape and texture in human newborns: evidence from cross-modal transfer tasks
Coralie Sann and Arlette Streri. 2007 · 2007
Earlier work this paper cites.
Frustratingly easy domain adaptation
Hal Daumé III. 2009 · 2009
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010 · 2010
Earlier work this paper cites.
Adapting visual category models to new domains. In European conference on computer vision . Springer, 213–226
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell. 2010 · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Semi-supervised domain adaptation with instance constraints. In Proceedings of the IEEE conference on computer vision and pattern recognition . 668–675
Jeff Donahue, Judy Hoffman, Erik Rodner, Kate Saenko, and Trevor Darrell. 2013 · 2013
Earlier work this paper cites.
Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning . PMLR, 647–655
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. 2014 · 2014
Earlier work this paper cites.
Domain generalization for object recognition with multi-task autoencoders. In Proceedings of the IEEE international conference on computer vision . 2551–2559
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi. 2015 · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning . PMLR, 448–456
Sergey Ioffe and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Semi-supervised domain adaptation with subspace learning for visual recognition. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition . 2142–2150
Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, and Tao Mei. 2015 · 2015
Earlier work this paper cites.
Image style transfer using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2414–2423
Leon A Gatys, Alexander S Ecker, and Matthias Bethge. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou. 2016 · 2016
Earlier work this paper cites.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle. 2016 · 2016
Earlier work this paper cites.
Return of frustratingly easy domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 30
Baochen Sun, Jiashi Feng, and Kate Saenko. 2016 · 2016
Earlier work this paper cites.
Fast generalized distillation for semi-supervised domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 31
Shuang Ao, Xiang Li, and Charles Ling. 2017 · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning . PMLR, 1126–1135
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Cited alongside, same era.
Arbitrary style transfer in real-time with adaptive instance normalization. In Proceedings of the IEEE International Conference on Computer Vision . 1501–1510
Xun Huang and Serge Belongie. 2017 · 2017
Cited alongside, same era.
Deeper, Broader and Artier Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales. 2017 · 2017
Cited alongside, same era.
Unified Deep Supervised Domain Adaptation and Generalization
Saeid Motiian, Marco Piccirilli, D. Adjeroh, and Gianfranco Doretto. 2017 · 2017
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 23–30
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel. 2017 · 2017
Photorealistic Style Transfer via Wavelet Transforms
Jaejun Yoo, Youngjung Uh, Sanghyuk Chun, Byeongkyu Kang, and Jung-Woo Ha. 2019 · 2019
Later among the works it cites.
Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 2100–2110
Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong. 2019 · 2019
Later among the works it cites.
Deceptionnet: Network-driven domain randomization. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 532–541
Sergey Zakharov, Wadim Kehl, and Slobodan Ilic. 2019 · 2019
Later among the works it cites.
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization
Prithvijit Chattopadhyay, Y. Balaji, and Judy Hoffman. 2020 · 2020
Later among the works it cites.
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Cited alongside, same era.
Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition . 7167–7176
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. 2017 · 2017
Cited alongside, same era.
Deep hashing network for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 5018–5027
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan. 2017 · 2017
Cited alongside, same era.
Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa. 2018 · 2018
Cited alongside, same era.
Domain generalization with domain-specific aggregation modules. In German Conference on Pattern Recognition . Springer, 187–198
Antonio D’Innocente and Barbara Caputo. 2018 · 2018
Cited alongside, same era.
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Hyeonseob Nam and Hyo-Eun Kim. 2018 · 2018
Cited alongside, same era.
Two at once: Enhancing learning and generalization capacities via ibn-net. In Proceedings of the European Conference on Computer Vision (ECCV) . 464–479
Xingang Pan, Ping Luo, Jianping Shi, and Xiaoou Tang. 2018 · 2018
Cited alongside, same era.
Maximum classifier discrepancy for unsupervised domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition . 3723–3732
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada. 2018 · 2018
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
Later among the works it cites.
Self-Challenging Improves Cross-Domain Generalization. In ECCV
Zeyi Huang, Haohan Wang, E. Xing, and Dong Huang. 2020 · 2020
Later among the works it cites.
Supervised Contrastive Learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2020
Later among the works it cites.
Learning texture invariant representation for domain adaptation of semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 12975–12984
Myeongjin Kim and Hyeran Byun. 2020 · 2020
Later among the works it cites.
Permuted AdaIN: Enhancing the Representation of Local Cues in Image Classifiers
Oren Nuriel, Sagie Benaim, and Lior Wolf. 2020 · 2020
Later among the works it cites.
Learning to learn single domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 12556–12565
Fengchun Qiao, Long Zhao, and Xi Peng. 2020 · 2020
Later among the works it cites.
Learning to Optimize Domain Specific Normalization for Domain Generalization. In ECCV
Seonguk Seo, Yumin Suh, D. Kim, Jongwoo Han, and B. Han. 2020 · 2020
Later among the works it cites.
Informative dropout for robust representation learning: A shape-bias perspective. In International Conference on Machine Learning . PMLR, 8828–8839
Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, and Jingdong Wang. 2020 · 2020
Later among the works it cites.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel. 2020 · 2020
Later among the works it cites.
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization. In ECCV
Shujun Wang, Lequan Yu, Caizi Li, Chi-Wing Fu, and P. Heng. 2020 · 2020
Later among the works it cites.
Domain Adaptive Ensemble Learning
Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. 2020b · 2020
Later among the works it cites.
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 11580–11590
Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne T Kim, Seungryong Kim, and Jaegul Choo. 2021 · 2021
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Fsdr: Frequency space domain randomization for domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 6891–6902
Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. 2021 · 2021
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Uncertainty-guided model generalization to unseen domains. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 6790–6800
Fengchun Qiao and Xi Peng. 2021 · 2021
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Robust and generalizable visual representation learning via random convolutions
Zhenlin Xu, Deyi Liu, Junlin Yang, and Marc Niethammer. 2021a · 2021
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Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. 2021 · 2021
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