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Domain generalization (DG) is a prevalent problem in real-world applications, which aims to train well-generalized models for unseen target domains by utilizing several source domains.
Genetic K-means algorithm
K. Krishna and M. Narasimha Murty. 1999 · 1999
Earlier work this paper cites.
An overview of statistical learning theory
Vladimir N Vapnik. 1999 · 1999
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias. In Proceedings of the IEEE International Conference on Computer Vision . 1657–1664
Chen Fang, Ye Xu, and Daniel N Rockmore. 2013 · 2013
Earlier work this paper cites.
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
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.
Deep coral: Correlation alignment for deep domain adaptation. In European conference on computer vision . Springer, 443–450
Baochen Sun and Kate Saenko. 2016 · 2016
Earlier work this paper cites.
Deeper, broader and artier domain generalization. In Proceedings of the IEEE international conference on computer vision . 5542–5550
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales. 2017 · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Recognition in terra incognita. In Proceedings of the European conference on computer vision (ECCV) . 456–473
Sara Beery, Grant Van Horn, and Pietro Perona. 2018 · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Earlier work this paper cites.
Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese. 2018 · 2018
Earlier work this paper cites.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019 · 2019
Earlier work this paper cites.
Do imagenet classifiers generalize to imagenet?. In International Conference on Machine Learning . PMLR, 5389–5400
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. 2019 · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations. In International conference on machine learning . PMLR, 1597–1607
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
Earlier work this paper cites.
Identifying statistical bias in dataset replication. In International Conference on Machine Learning . PMLR, 2922–2932
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, and Aleksander Madry. 2020 · 2020
Earlier work this paper cites.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020 · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Self-challenging improves cross-domain generalization
Zeyi Huang, Haohan Wang, Eric P Xing, and Dong Huang. 2020 · 2020
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020 · 2020
Cited alongside, same era.
The role of disentanglement in generalisation. In International Conference on Learning Representations
Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, and Jeffrey Bowers. 2020 · 2020
Cited alongside, same era.
Efficient domain generalization via common-specific low-rank decomposition. In International Conference on Machine Learning . PMLR, 7728–7738
Vihari Piratla, Praneeth Netrapalli, and Sunita Sarawagi. 2020 · 2020
Cited alongside, same era.
Distributionally Robust Neural Networks. In International Conference on Learning Representations
Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang. 2020 · 2020
Cited alongside, same era.
Improve unsupervised domain adaptation with mixup training
Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, and Liu Ren. 2020 · 2020
Invariant causal mechanisms through distribution matching
Mathieu Chevalley, Charlotte Bunne, Andreas Krause, and Stefan Bauer. 2022 · 2022
Later among the works it cites.
Hero: Hierarchical spatio-temporal reasoning with contrastive action correspondence for end-to-end video object grounding. In Proceedings of the 30th ACM International Conference on Multimedia . 3801–3810
Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Wenqiao Zhang, Jiaxu Miao, Shiliang Pu, and Fei Wu. 2022 · 2022
Later among the works it cites.
Decorr: Environment Partitioning for Invariant Learning and OOD Generalization
Yufan Liao, Qi Wu, and Xing Yan. 2022 · 2022
Later among the works it cites.
ZIN: When and How to Learn Invariance Without Environment Partition?
Yong Lin, Shengyu Zhu, Lu Tan, and Peng Cui. 2022b · 2022
Later among the works it cites.
Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling
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Cited alongside, same era.
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
Marvin Zhang, Henrik Marklund, Abhishek Gupta, Sergey Levine, and Chelsea Finn. 2020 · 2020
Cited alongside, same era.
Domain generalization with optimal transport and metric learning
Fan Zhou, Zhuqing Jiang, Changjian Shui, Boyu Wang, and Brahim Chaib-draa. 2020 · 2020
Cited alongside, same era.
Decaug: Out-of-distribution generalization via decomposed feature representation and semantic augmentation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 6705–6713
Haoyue Bai, Rui Sun, Lanqing Hong, Fengwei Zhou, Nanyang Ye, Han-Jia Ye, S-H Gary Chan, and Zhenguo Li. 2021 · 2021
Cited alongside, same era.
Domain Generalization by Marginal Transfer Learning
Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Scott. 2021 · 2021
Cited alongside, same era.
SWAD: Domain Generalization by Seeking Flat Minima. In Advances in Neural Information Processing Systems (NeurIPS)
Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. 2021 · 2021
Cited alongside, same era.
Environment Inference for Invariant Learning. In International Conference on Machine Learning
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel. 2021 · 2021
Cited alongside, same era.
In Search of Lost Domain Generalization. In International Conference on Learning Representations
Ishaan Gulrajani and David Lopez-Paz. 2021 · 2021
Cited alongside, same era.
Zheqi Lv, Feng Wang, Shengyu Zhang, Kun Kuang, Hongxia Yang, and Fei Wu. 2022b · 2022
Later among the works it cites.
Domain Generalization via Contrastive Causal Learning
Qiaowei Miao, Junkun Yuan, and Kun Kuang. 2022 · 2022
Later among the works it cites.
Gradient Matching for Domain Generalization. In International Conference on Learning Representations
Yuge Shi, Jeffrey Seely, Philip Torr, Siddharth N, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve. 2022 · 2022
Later among the works it cites.
Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1787–1797
Christopher Tran and Elena Zheleva. 2022 · 2022
Later among the works it cites.
Learning Decomposed Representations for Treatment Effect Estimation
Anpeng Wu, Junkun Yuan, Kun Kuang, Bo Li, Runze Wu, Qiang Zhu, Yueting Zhuang, and Fei Wu. 2023 · 2022
Later among the works it cites.
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization. In Proceedings of the 30th ACM International Conference on Multimedia . 2361–2370
Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, and Lanfen Lin. 2022 · 2022
Later among the works it cites.
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XX . Springer, 453–470
Min Zhang, Siteng Huang, Wenbin Li, and Donglin Wang. 2022a · 2022
Later among the works it cites.
Measure the Predictive Heterogeneity. In International Conference on Learning Representations
Jiashuo Liu, Jiayun Wu, Renjie Pi, Renzhe Xu, Xingxuan Zhang, Bo Li, and Peng Cui. 2023 · 2023
Closest in time.
IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System
Zheqi Lv, Zhengyu Chen, Shengyu Zhang, Kun Kuang, Wenqiao Zhang, Mengze Li, Beng Chin Ooi, and Fei Wu. 2023a · 2023
Closest in time.
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization. In Proceedings of the ACM Web Conference 2023
Zheqi Lv, Wenqiao Zhang, Shengyu Zhang, Kun Kuang, Feng Wang, Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang, Beng Chin Ooi, and Fei Wu. 2023b · 2023
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Knowledge Distillation-based Domain-invariant Representation Learning for Domain Generalization
Ziwei Niu, Junkun Yuan, Xu Ma, Yingying Xu, Jing Liu, Yen-Wei Chen, Ruofeng Tong, and Lanfen Lin. 2023 · 2023
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Domain-specific bias filtering for single labeled domain generalization
Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, and Lanfen Lin. 2023a · 2023
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Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
Junkun Yuan, Xu Ma, Ruoxuan Xiong, Mingming Gong, Xiangyu Liu, Fei Wu, Lanfen Lin, and Kun Kuang. 2023b · 2023
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Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes. In The Eleventh International Conference on Learning Representations
Fengda Zhang, Kun Kuang, Long Chen, Yuxuan Liu, Chao Wu, and Jun Xiao. 2023 · 2023
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Universal Domain Adaptation via Compressive Attention Matching
Didi Zhu, Yincuan Li, Junkun Yuan, Zexi Li, Yunfeng Shao, Kun Kuang, and Chao Wu. 2023 · 2023
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Differentiated matching for individual and average treatment effect estimation
Zhao Ziyu, Kun Kuang, Bo Li, Peng Cui, Runze Wu, Jun Xiao, and Fei Wu. 2023 · 2023
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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 · 2030
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