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Domain generalization (DG), aiming to make models work on unseen domains, is a surefire way toward general artificial intelligence.
L. Fei-Fei, R. Fergus, and P. Perona, “Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,” in 2004 Conference on Computer Vision and Pattern Recognition Workshop , 2004, pp. 178–178
2004
Earlier work this paper cites.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science , vol. 313, no. 5786, pp. 504–507, 2006
2006
Earlier work this paper cites.
B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, “Labelme: a database and web-based tool for image annotation,” International journal of computer vision , vol. 77, no. 1, pp. 157–173, 2008
2008
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
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 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
A. Torralba and A. A. Efros, “Unbiased look at dataset bias,” in CVPR 2011 , 2011, pp. 1521–1528
2011
Earlier work this paper cites.
A. Khosla, T. Zhou, T. Malisiewicz, A. A. Efros, and A. Torralba, “Undoing the damage of dataset bias,” in European Conference on Computer Vision . Springer, 2012, pp. 158–171
2012
Earlier work this paper cites.
K. Muandet, D. Balduzzi, and B. Schölkopf, “Domain generalization via invariant feature representation,” in Proceedings of the 30th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 28, no. 1. PMLR, 17–19 Jun 2013, pp. 10–18
2013
Earlier work this paper cites.
C. Fang, Y. Xu, and D. N. Rockmore, “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 (ICCV) , December 2013
2013
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, May 2015
2015
Earlier work this paper cites.
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh, “Vqa: Visual question answering,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2425–2433
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
B. Sun and K. Saenko, “Deep coral: Correlation alignment for deep domain adaptation,” in European conference on computer vision . Springer, 2016, pp. 443–450
2016
Earlier work this paper cites.
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales, “Deeper, broader and artier domain generalization,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , Oct 2017
2017
Earlier work this paper cites.
H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan, “Deep hashing network for unsupervised domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
2017
Earlier work this paper cites.
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales, “Deeper, broader and artier domain generalization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 5542–5550
2017
Earlier work this paper cites.
Y. Li, X. Tian, M. Gong, Y. Liu, T. Liu, K. Zhang, and D. Tao, “Deep domain generalization via conditional invariant adversarial networks,” in Proceedings of the European Conference on Computer Vision (ECCV) , September 2018
2018
Earlier work this paper cites.
S. Beery, G. Van Horn, and P. Perona, “Recognition in terra incognita,” in Proceedings of the European Conference on Computer Vision (ECCV) , September 2018
2018
Cited alongside, same era.
H. Li, S. J. Pan, S. Wang, and A. C. Kot, “Domain generalization with adversarial feature learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
Cited alongside, same era.
F. M. Carlucci, A. D’Innocente, S. Bucci, B. Caputo, and T. Tommasi, “Domain generalization by solving jigsaw puzzles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
Cited alongside, same era.
H. Wang, Z. He, and E. P. Xing, “Learning robust representations by projecting superficial statistics out,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
Z. Wang, Y. Luo, R. Qiu, Z. Huang, and M. Baktashmotlagh, “Learning to diversify for single domain generalization,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 834–843
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning . PMLR, 2021, pp. 8748–8763
2021
Later among the works it cites.
G. Goh, N. Cammarata, C. Voss, S. Carter, M. Petrov, L. Schubert, A. Radford, and C. Olah, “Multimodal neurons in artificial neural networks,” Distill , vol. 6, no. 3, p. e30, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
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X. Peng, Q. Bai, X. Xia, Z. Huang, K. Saenko, and B. Wang, “Moment matching for multi-source domain adaptation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
J. Lu, D. Batra, D. Parikh, and S. Lee, “Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
R. Zellers, Y. Bisk, A. Farhadi, and Y. Choi, “From recognition to cognition: Visual commonsense reasoning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 6720–6731
2019
Cited alongside, same era.
X. Peng, Q. Bai, X. Xia, Z. Huang, K. Saenko, and B. Wang, “Moment matching for multi-source domain adaptation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 1406–1415
2019
Cited alongside, same era.
2019
Cited alongside, same era.
K. Zhou, Y. Yang, T. Hospedales, and T. Xiang, “Learning to generate novel domains for domain generalization,” in European conference on computer vision . Springer, 2020, pp. 561–578
2020
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
K. Zhou, Y. Yang, Y. Qiao, and T. Xiang, “Domain generalization with mixstyle,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
I. Gulrajani and D. Lopez-Paz, “In search of lost domain generalization,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
H. Nam, H. Lee, J. Park, W. Yoon, and D. Yoo, “Reducing domain gap by reducing style bias,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 8690–8699
2021
Later among the works it cites.
D. Kim, Y. Yoo, S. Park, J. Kim, and J. Lee, “Selfreg: Self-supervised contrastive regularization for domain generalization,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 9619–9628
2021
Later among the works it cites.
K. Zhou, Z. Liu, Y. Qiao, T. Xiang, and C. C. Loy, “Domain generalization: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Closest in time.
X. Zhang, L. Zhou, R. Xu, P. Cui, Z. Shen, and H. Liu, “Towards unsupervised domain generalization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 4910–4920
2022
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2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Y. Shi, J. Seely, P. Torr, S. N, A. Hannun, N. Usunier, and G. Synnaeve, “Gradient matching for domain generalization,” in International Conference on Learning Representations , 2022
2022
Closest in time.