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Domain Generalization (DG) aims to generalize a model trained on multiple source domains to an unseen target domain.
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. Efros, and T. Darrell, “Cycada: Cycle-consistent adversarial domain adaptation,” in ICML , 2018, pp. 1989–1998
1998
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
Y. Grandvalet, Y. Bengio et al. , “Semi-supervised learning by entropy minimization.” CAP , vol. 367, pp. 281–296, 2005
2005
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in CVPR , 2009, pp. 248–255
2009
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in NeurlPS workshops , 2011, p. 5
2011
Earlier work this paper cites.
D.-H. Lee et al. , “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in ICML workshop , vol. 3, no. 2, 2013, p. 896
2013
Earlier work this paper cites.
M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in ICML , 2015, pp. 97–105
2015
Earlier work this paper cites.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in ICML , 2015, pp. 1180–1189
2015
Earlier work this paper cites.
M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in ICML , 2015, pp. 97–105
2015
Earlier work this paper cites.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” JMLR , vol. 17, no. 1, pp. 2096–2030, 2016
2016
Earlier work this paper cites.
M.-Y. Liu and O. Tuzel, “Coupled generative adversarial networks,” in NeurIPS , 2016, pp. 469–477
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales, “Deeper, broader and artier domain generalization,” in ICCV , 2017, pp. 5542–5550
2017
Earlier work this paper cites.
H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan, “Deep hashing network for unsupervised domain adaptation,” in CVPR , 2017, pp. 5018–5027
2017
Earlier work this paper cites.
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan, “Unsupervised pixel-level domain adaptation with generative adversarial networks,” in CVPR , 2017, pp. 3722–3731
2017
Earlier work this paper cites.
S. Laine and T. Aila, “Temporal ensembling for semi-supervised learning,” in ICLR , 2017
2017
Earlier work this paper cites.
A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” in NeurIPS , 2017, pp. 1195–1204
2017
Earlier work this paper cites.
D. Arpit, S. Jastrzębski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio et al. , “A closer look at memorization in deep networks,” in ICML , 2017, pp. 233–242
2017
Earlier work this paper cites.
M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Deep transfer learning with joint adaptation networks,” in ICML , 2017, pp. 2208–2217
2017
Earlier work this paper cites.
S. Shankar, V. Piratla, S. Chakrabarti, S. Chaudhuri, P. Jyothi, and S. Sarawagi, “Generalizing across domains via cross-gradient training,” in ICLR , 2018
2018
Earlier work this paper cites.
K. Saito, K. Watanabe, Y. Ushiku, and T. Harada, “Maximum classifier discrepancy for unsupervised domain adaptation,” in CVPR , 2018, pp. 3723–3732
2018
Cited alongside, same era.
Y. Li, X. Tian, M. Gong, Y. Liu, T. Liu, K. Zhang, and D. Tao, “Deep domain generalization via conditional invariant adversarial networks,” in ECCV , 2018, pp. 624–639
2018
Cited alongside, same era.
Y. Balaji, S. Sankaranarayanan, and R. Chellappa, “Metareg: Towards domain generalization using meta-regularization,” NeurIPS , vol. 31, pp. 998–1008, 2018
2018
Cited alongside, same era.
T. Miyato, S.-i. Maeda, M. Koyama, and S. Ishii, “Virtual adversarial training: a regularization method for supervised and semi-supervised learning,” TPAMI , vol. 41, no. 8, pp. 1979–1993, 2018
2018
Cited alongside, same era.
L. Jiang, Z. Zhou, T. Leung, L.-J. Li, and L. Fei-Fei, “Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels,” in ICML , 2018, pp. 2304–2313
F. Qiao, L. Zhao, and X. Peng, “Learning to learn single domain generalization,” in CVPR , 2020, pp. 12 556–12 565
2020
Later among the works it cites.
Z. Lu, Y. Yang, X. Zhu, C. Liu, Y.-Z. Song, and T. Xiang, “Stochastic classifiers for unsupervised domain adaptation,” in CVPR , 2020, pp. 9111–9120
2020
Later among the works it cites.
C.-W. Kuo, C.-Y. Ma, J.-B. Huang, and Z. Kira, “Featmatch: Feature-based augmentation for semi-supervised learning,” in ECCV , 2020, pp. 479–495
2020
Later among the works it cites.
K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, and C.-L. Li, “Fixmatch: Simplifying semi-supervised learning with consistency and confidence,” NeurIPS , vol. 33, 2020
2020
Later among the works it cites.
J. Cha, S. Chun, K. Lee, H.-C. Cho, S. Park, Y. Lee, and S. Park, “Swad: Domain generalization by seeking flat minima,” NeurIPS , vol. 34, 2021
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2018
Cited alongside, same era.
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama, “Co-teaching: Robust training of deep neural networks with extremely noisy labels,” in NeurIPS , 2018, pp. 8527–8537
2018
Cited alongside, same era.
H. Nam and H.-E. Kim, “Batch-instance normalization for adaptively style-invariant neural networks,” NeurIPS , vol. 31, 2018
2018
Cited alongside, same era.
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” in ICLR , 2018
2018
Cited alongside, same era.
P. Roy, S. Bhattacharya, and S. Ghosh, “Synthetic digits,” 2018, https://www.kaggle.com/prasunroy/synthetic-digits
2018
Cited alongside, same era.
M. Long, Z. Cao, J. Wang, and M. I. Jordan, “Conditional adversarial domain adaptation,” in NeurIPS , 2018, pp. 1647–1657
2018
Cited alongside, same era.
2018
Cited alongside, same era.
D. Li, J. Zhang, Y. Yang, C. Liu, Y.-Z. Song, and T. M. Hospedales, “Episodic training for domain generalization,” in ICCV , 2019, pp. 1446–1455
2019
Cited alongside, same era.
2021
Closest in time.
Q. Xu, R. Zhang, Y. Zhang, Y. Wang, and Q. Tian, “A fourier-based framework for domain generalization,” in CVPR , 2021, pp. 14 383–14 392
2021
Closest in time.
Z. Wang, Y. Luo, R. Qiu, Z. Huang, and M. Baktashmotlagh, “Learning to diversify for single domain generalization,” in ICCV , 2021, pp. 834–843
2021
Closest in time.
X. Fan, Q. Wang, J. Ke, F. Yang, B. Gong, and M. Zhou, “Adversarially adaptive normalization for single domain generalization,” in CVPR , 2021, pp. 8208–8217
2021
Closest in time.
W. Wang, H. Li, Z. Ding, F. Nie, J. Chen, X. Dong, and Z. Wang, “Rethinking maximum mean discrepancy for visual domain adaptation,” TNNLS , 2021
2021
Closest in time.
L. Mansilla, R. Echeveste, D. H. Milone, and E. Ferrante, “Domain generalization via gradient surgery,” in ICCV , 2021, pp. 6630–6638
2021
Closest in time.
K. Zhou, Y. Yang, Y. Qiao, and T. Xiang, “Domain adaptive ensemble learning,” TIP , vol. 30, pp. 8008–8018, 2021
2021
Closest in time.
Z. Sun, Z. Shen, L. Lin, Y. Yu, Z. Yang, S. Yang, and W. Chen, “Dynamic domain generalization,” in IJCAI , 2022
2022
Closest in time.
C. Jia and Y. Zhang, “Meta-learning the invariant representation for domain generalization,” Machine Learning , pp. 1–21, 2022
2022
Closest in time.
X. Li, Y. Dai, Y. Ge, J. Liu, Y. Shan, and L. DUAN, “Uncertainty modeling for out-of-distribution generalization,” in ICLR , 2022
2022
Closest in time.
L. Chen, H. Chen, Z. Wei, X. Jin, X. Tan, Y. Jin, and E. Chen, “Reusing the task-specific classifier as a discriminator: Discriminator-free adversarial domain adaptation,” in CVPR , 2022, pp. 7181–7190
2022
Closest in time.
C. Yang, B. Xue, K. C. Tan, and M. Zhang, “A co-training framework for heterogeneous heuristic domain adaptation,” TNNLS , 2022
2022
Closest in time.
J. Huang, N. Xiao, and L. Zhang, “Balancing transferability and discriminability for unsupervised domain adaptation,” TNNLS , 2022
2022
Closest in time.
W. Chen, L. Lin, S. Yang, D. Xie, S. Pu, and Y. Zhuang, “Self-supervised noisy label learning for source-free unsupervised domain adaptation,” in IROS , 2022, pp. 10 185–10 192
2022
Closest in time.
H. Song, M. Kim, D. Park, Y. Shin, and J.-G. Lee, “Learning from noisy labels with deep neural networks: A survey,” TNNLS , 2022
2022
Closest in time.
N. Xu, J.-Y. Li, Y.-P. Liu, and X. Geng, “Trusted-data-guided label enhancement on noisy labels,” TNNLS , 2022
2022
Closest in time.