Fetching the paper…
Reading the bibliography…
In domain generalization, multiple labeled non-independent and non-identically distributed source domains are available during training while neither the data nor the labels of target domains are.
G. Widmer and M. Kubat, “Learning in the presence of concept drift and hidden contexts,” Machine learning , vol. 23, no. 1, pp. 69–101, 1996
1996
Earlier work this paper cites.
H. Shimodaira, “Improving predictive inference under covariate shift by weighting the log-likelihood function,” Journal of statistical planning and inference , vol. 90, no. 2, pp. 227–244, 2000
2000
Earlier work this paper cites.
D. M. Endres and J. E. Schindelin, “A new metric for probability distributions,” IEEE IT , vol. 49, no. 7, pp. 1858–1860, 2003
2003
Earlier work this paper cites.
B. Zadrozny, “Learning and evaluating classifiers under sample selection bias,” in ICML , 2004, p. 114
2004
Earlier work this paper cites.
L. Fei-Fei et al. , “Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,” in CVPR workshop . IEEE, 2004, pp. 178–178
2004
Earlier work this paper cites.
G. I. Webb and K. M. Ting, “On the application of roc analysis to predict classification performance under varying class distributions,” Machine learning , vol. 58, no. 1, pp. 25–32, 2005
2005
Earlier work this paper cites.
K. M. Borgwardt et al. , “Integrating structured biological data by kernel maximum mean discrepancy,” Bioinformatics , vol. 22, no. 14, pp. e49–e57, 2006
2006
Earlier work this paper cites.
S. Ben-David et al. , “Analysis of representations for domain adaptation,” in NeurIPS , 2007, pp. 137–144
2007
Earlier work this paper cites.
M. Dredze and K. Crammer, “Online methods for multi-domain learning and adaptation,” in EMNLP , 2008, pp. 689–697
2008
Earlier work this paper cites.
B. C. Russell et al. , “Labelme: a database and web-based tool for image annotation,” IJCV , vol. 77, no. 1-3, pp. 157–173, 2008
2008
Earlier work this paper cites.
J. Quionero-Candela et al. , Dataset shift in machine learning . MIT Press, 2009
2009
Earlier work this paper cites.
H. Daumé III, “Frustratingly easy domain adaptation,” arXiv:0907.1815 , 2009
2009
Earlier work this paper cites.
Y. Mansour et al. , “Domain adaptation with multiple sources,” in NeurIPS , 2009, pp. 1041–1048
2009
Earlier work this paper cites.
——, “A theory of learning from different domains,” Machine learning , vol. 79, no. 1-2, pp. 151–175, 2010
2010
Earlier work this paper cites.
——, “Impossibility theorems for domain adaptation,” in International Conference on Artificial Intelligence and Statistics , 2010, pp. 129–136
2010
Earlier work this paper cites.
M. Everingham et al. , “The pascal visual object classes (voc) challenge,” IJCV , vol. 88, no. 2, pp. 303–338, 2010
2010
Earlier work this paper cites.
2010
Cited alongside, same era.
J. G. Moreno-Torres et al. , “A unifying view on dataset shift in classification,” Patt.Recog. , vol. 45, no. 1, pp. 521–530, 2012
2012
Cited alongside, same era.
J. Hoffman et al. , “Discovering latent domains for multisource domain adaptation,” in ECCV . Springer, 2012, pp. 702–715
2012
Cited alongside, same era.
C. V. Dinh et al. , “Fidos: A generalized fisher based feature extraction method for domain shift,” Patt.Recog. , vol. 46, no. 9, pp. 2510–2518, 2013
2013
Cited alongside, same era.
B. Fernando et al. , “Unsupervised visual domain adaptation using subspace alignment,” in ICCV , 2013, pp. 2960–2967
2013
Cited alongside, same era.
N. Courty et al. , “Joint distribution optimal transportation for domain adaptation,” in NeurIPS , 2017, pp. 3730–3739
2017
Later among the works it cites.
Q. Xie et al. , “Controllable invariance through adversarial feature learning,” in NeurIPS , 2017, pp. 585–596
2017
Later among the works it cites.
A. Achille and S. Soatto, “Emergence of invariance and disentanglement in deep representations,” JMLR , vol. 19, no. 1, pp. 1947–1980, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Shankar et al. , “Generalizing across domains via cross-gradient training,” arXiv: 1804.10745 , 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Yu et al. , “Kl-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition,” in ICASSP . IEEE, 2013, pp. 7893–7897
2013
Cited alongside, same era.
K. Muandet et al. , “Domain generalization via invariant feature representation,” in ICML , 2013, pp. 10–18
2013
Cited alongside, same era.
C. Fang et al. , “Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias,” in ICCV , 2013, pp. 1657–1664
2013
Cited alongside, same era.
Z. Xu et al. , “Exploiting low-rank structure from latent domains for domain generalization,” in ECCV . Springer, 2014, pp. 628–643
2014
Cited alongside, same era.
M. Ghifary et al. , “Domain generalization for object recognition with multi-task autoencoders,” in ICCV , 2015, pp. 2551–2559
2015
Cited alongside, same era.
K. Zhang et al. , “Multi-source domain adaptation: A causal view,” in AAAI , 2015
2015
Cited alongside, same era.
M. Ghifary et al. , “Scatter component analysis: A unified framework for domain adaptation and domain generalization,” IEEE TPAMI , vol. 39, no. 7, pp. 1414–1430, 2016
2016
Cited alongside, same era.
B. Bhushan Damodaran et al. , “Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation,” in ECCV , 2018, pp. 447–463
2018
Later among the works it cites.
H. Li et al. , “Domain generalization with adversarial feature learning,” in CVPR , 2018, pp. 5400–5409
2018
Later among the works it cites.
Y. Li et al. , “Deep domain generalization via conditional invariant adversarial networks,” in ECCV , 2018, pp. 624–639
2018
Later among the works it cites.
D. Li et al. , “Learning to generalize: Meta-learning for domain generalization,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Later among the works it cites.
W. M. Kouw and M. Loog, “A review of domain adaptation without target labels,” IEEE TPAMI , 2019
2019
Later among the works it cites.
M. Ilse et al. , “Diva: Domain invariant variational autoencoders,” arXiv:1905.10427 , 2019
2019
Later among the works it cites.
S. Hu et al. , “Domain generalization via multidomain discriminant analysis,” in Uncertainty in artificial intelligence: proceedings of the… conference. Conference on Uncertainty in Artificial Intelligence , vol. 35. NIH Public Access, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
——, “Episodic training for domain generalization,” in ICCV , 2019, pp. 1446–1455
2019
Later among the works it cites.