Fetching the paper…
Reading the bibliography…
For many practical computer vision applications, the learned models usually have high performance on the datasets used for training but suffer from significant performance degradation when deployed in new environments, where there are usually style differences between the training images and the testing images.
X. Wang, Y. Jin, M. Long, J. Wang, and M. I. Jordan, “Transferable normalization: Towards improving transferability of deep neural networks,” in NeurIPS , 2019, pp. 1951–1961
1961
Earlier work this paper cites.
X. Peng and K. Saenko, “Synthetic to real adaptation with generative correlation alignment networks,” in WACV . IEEE, 2018, pp. 1982–1991
1991
Earlier work this paper cites.
J. J. Hull, “A database for handwritten text recognition research,” TPAMI , vol. 16, no. 5, pp. 550–554, 1994
1994
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” in IEEE , 1998
1998
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” 2008
2008
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering , vol. 22, no. 10, pp. 1345–1359, 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li, “Imagenet: A large-scale hierarchical image database,” in CVPR , 2009
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 NeurIPS-W , 2011
2011
Earlier work this paper cites.
W.-S. Zheng, S. Gong, and T. Xiang, “Person re-identification by probabilistic relative distance comparison,” 2011
2011
Earlier work this paper cites.
A. Torralba and A. A. Efros, “Unbiased look at dataset bias,” in CVPR 2011 . IEEE, 2011, pp. 1521–1528
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NeurIPS , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
K. Muandet, D. Balduzzi, and B. Schölkopf, “Domain generalization via invariant feature representation,” in International Conference on Machine Learning , 2013, pp. 10–18
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” IJRR , 2013
2013
Earlier work this paper cites.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” ICML , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in CVPR , 2014, pp. 580–587
2014
Earlier work this paper cites.
M. Long, Y. Cao, J. Wang, and M. I. Jordan, “Learning transferable features with deep adaptation networks,” ICML , 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in NeurIPS , 2015, pp. 91–99
2015
Earlier work this paper cites.
Y. Ganin and V. S. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in ICML , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Unsupervised domain adaptation with residual transfer networks,” in NeurIPS , 2016, pp. 136–144
2016
Earlier work this paper cites.
B. Sun, J. Feng, and K. Saenko, “Return of frustratingly easy domain adaptation,” in AAAI , 2016
2016
Earlier work this paper cites.
B. Sun and K. Saenko, “Deep coral: Correlation alignment for deep domain adaptation,” in ECCV , 2016, pp. 443–450
2016
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,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 2096–2030, 2016
2016
Earlier work this paper cites.
M. Ghifary, D. Balduzzi, W. B. Kleijn, and M. Zhang, “Scatter component analysis: A unified framework for domain adaptation and domain generalization,” TPAMI , vol. 39, no. 7, pp. 1414–1430, 2016
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.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in CVPR , 2016
2016
Earlier work this paper cites.
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez, “The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in CVPR , 2016, pp. 3234–3243
2016
Earlier work this paper cites.
S. R. Richter, V. Vineet, S. Roth, and V. Koltun, “Playing for data: Ground truth from computer games,” in ECCV , ser. LNCS, B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., vol. 9906. Springer International Publishing, 2016, pp. 102–118
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Cited alongside, same era.
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales, “Deeper, broader and artier domain generalization,” in ICCV , 2017, pp. 5542–5550
2017
Cited alongside, same era.
W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, and S. Saminger-Platz, “Central moment discrepancy (cmd) for domain-invariant representation learning,” CoRR , 2017
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in CVPR , 2018, pp. 7132–7141
2018
Later among the works it cites.
H. Li, S. Jialin Pan, S. Wang, and A. C. Kot, “Domain generalization with adversarial feature learning,” in CVPR , 2018, pp. 5400–5409
2018
Later among the works it cites.
R. Xu, Z. Chen, W. Zuo, J. Yan, and L. Lin, “Deep cocktail network: Multi-source unsupervised domain adaptation with category shift,” in CVPR , 2018, pp. 3964–3973
2018
Later among the works it cites.
J. Wang, W. Feng, Y. Chen, H. Yu, M. Huang, and P. S. Yu, “Visual domain adaptation with manifold embedded distribution alignment,” in ACMMM , 2018, pp. 402–410
2018
Later among the works it cites.
Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker, “Learning to adapt structured output space for semantic segmentation,” in CVPR , 2018, pp. 7472–7481
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Deep transfer learning with joint adaptation networks,” in ICML , 2017, pp. 2208–2217
2017
Cited alongside, same era.
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in CVPR , 2017, pp. 7167–7176
2017
Cited alongside, same era.
S. Motiian, M. Piccirilli, D. A. Adjeroh, and G. Doretto, “Unified deep supervised domain adaptation and generalization,” in ICCV , 2017, pp. 5715–5725
2017
Cited alongside, same era.
X. Huang and S. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” in ICCV , 2017, pp. 1501–1510
2017
Cited alongside, same era.
D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis,” in CVPR , 2017, pp. 6924–6932
2017
Cited alongside, same era.
V. Dumoulin, J. Shlens, and M. Kudlur, “A learned representation for artistic style,” ICLR , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Later among the works it cites.
Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool, “Domain adaptive faster r-cnn for object detection in the wild,” in CVPR , 2018, pp. 3339–3348
2018
Later among the works it cites.
C. Sakaridis, D. Dai, and L. Van Gool, “Semantic foggy scene understanding with synthetic data,” IJCV , pp. 1–20, 2018
2018
Later among the works it cites.
Y. Tsai, W. Hung, S. Schulter, K. Sohn, M. Yang, and M. Chandraker, “Learning to adapt structured output space for semantic segmentation,” in CVPR , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
X. Ma, T. Zhang, and C. Xu, “Deep multi-modality adversarial networks for unsupervised domain adaptation,” IEEE TMM , vol. 21, no. 9, pp. 2419–2431, 2019
2019
Later among the works it cites.
F. M. Carlucci, A. D’Innocente, S. Bucci, B. Caputo, and T. Tommasi, “Domain generalization by solving jigsaw puzzles,” in CVPR , 2019, pp. 2229–2238
2019
Later among the works it cites.
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
Later among the works it cites.
X. Peng, Q. Bai, X. Xia, Z. Huang, K. Saenko, and B. Wang, “Moment matching for multi-source domain adaptation,” in ICCV , 2019, pp. 1406–1415
2019
Later among the works it cites.
R. Xu, G. Li, J. Yang, and L. Lin, “Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation,” in ICCV , 2019, pp. 1426–1435
2019
Later among the works it cites.
H. Liu, M. Long, J. Wang, and M. Jordan, “Transferable adversarial training: A general approach to adapting deep classifiers,” in ICML , 2019, pp. 4013–4022
2019
Later among the works it cites.
J. Jia, Q. Ruan, and T. M. Hospedales, “Frustratingly easy person re-identification: Generalizing person re-id in practice,” 2019
2019
Later among the works it cites.
J. Song, Y. Yang, Y.-Z. Song, T. Xiang, and T. M. Hospedales, “Generalizable person re-identification by domain-invariant mapping network,” 2019
2019
Later among the works it cites.
K. Zhou, Y. Yang, A. Cavallaro et al. , “Omni-scale feature learning for person re-identification,” 2019
2019
Later among the works it cites.
H. Yan, Z. Li, Q. Wang, P. Li, Y. Xu, and W. Zuo, “Weighted and class-specific maximum mean discrepancy for unsupervised domain adaptation,” IEEE TMM , vol. 22, no. 9, pp. 2420–2433, 2019
2019
Later among the works it cites.
K. Saito, D. Kim, S. Sclaroff, T. Darrell, and K. Saenko, “Semi-supervised domain adaptation via minimax entropy,” in ICCV , 2019, pp. 8050–8058
2019
Later among the works it cites.
T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. Pérez, “Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,” in CVPR , 2019, pp. 2517–2526
2019
Later among the works it cites.
M. Khodabandeh, A. Vahdat, M. Ranjbar, and W. G. Macready, “A robust learning approach to domain adaptive object detection,” in ICCV , 2019, pp. 480–490
2019
Later among the works it cites.
F. M. Carlucci, A. D’Innocente, S. Bucci, B. Caputo, and T. Tommasi, “Domain generalization by solving jigsaw puzzles,” in CVPR , 2019
2019
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
X. Peng, Z. Huang, Y. Zhu, and K. Saenko, “Federated adversarial domain adaptation,” ICLR , 2020
2020
Later among the works it cites.
K. Zhou, Y. Yang, T. Hospedales, and T. Xiang, “Learning to generate novel domains for domain generalization,” in ECCV . Springer, 2020, pp. 561–578
2020
Later among the works it cites.
Y. Zhang, P. David, and B. Gong, “Curriculum domain adaptation for semantic segmentation of urban scenes,” in ICCV , 2017, pp. 2020–2030
2030
Closest in time.
M. Chen, H. Xue, and D. Cai, “Domain adaptation for semantic segmentation with maximum squares loss,” in ICCV , 2019, pp. 2090–2099
2099
Closest in time.