S. J. Pan and Q. Yang, A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010.
DOI : 10.1109/TKDE.2009.191
URL : http://www.cs.ust.hk/~sinnopan/publications/TLsurvey_0822.pdf

S. Ben-david, J. Blitzer, K. Crammer, and F. Pereira, Analysis of representations for domain adaptation, NIPS, pp.137-144, 2006.

E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko, Adversarial Discriminative Domain Adaptation, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
DOI : 10.1109/CVPR.2017.316
URL : http://arxiv.org/pdf/1702.05464

Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle et al., Domain-Adversarial Training of Neural Networks, JMLR, vol.17, issue.59, pp.1-35, 2016.
DOI : 10.1007/978-3-319-58347-1_10
URL : https://hal.archives-ouvertes.fr/hal-01624607

B. Sun, J. Feng, and K. Saenko, Return of frustratingly easy domain adaptation, AAAI, pp.2058-2065, 2016.

B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, Unsupervised Visual Domain Adaptation Using Subspace Alignment, 2013 IEEE International Conference on Computer Vision, pp.2960-2967, 2013.
DOI : 10.1109/ICCV.2013.368
URL : https://hal.archives-ouvertes.fr/hal-00869417

B. Gong, Y. Shi, F. Sha, and K. Grauman, Geodesic flow kernel for unsupervised domain adaptation, CVPR, pp.2066-2073, 2012.

R. Gopalan, R. Li, and R. Chellappa, Domain adaptation for object recognition: An unsupervised approach, 2011 International Conference on Computer Vision, pp.999-1006, 2011.
DOI : 10.1109/ICCV.2011.6126344
URL : http://www.umiacs.umd.edu/~raghuram/Publications/2011_ICCV_DomainAdaptation.pdf

S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, Domain Adaptation via Transfer Component Analysis, IJCAI, pp.1187-1192, 2009.
DOI : 10.1109/TNN.2010.2091281
URL : http://ijcai.org/papers09/Papers/IJCAI09-200.pdf

N. Courty, R. Flamary, and D. Tuia, Domain Adaptation with Regularized Optimal Transport, ECML/PKDD, 2014.
DOI : 10.1007/978-3-662-44848-9_18
URL : https://hal.archives-ouvertes.fr/hal-01018698

N. Courty, R. Flamary, D. Tuia, and A. Rakotomamonjy, Optimal Transport for Domain Adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, issue.9, 2016.
DOI : 10.1109/TPAMI.2016.2615921
URL : https://hal.archives-ouvertes.fr/hal-01170705

N. T. Gayraud, A. Rakotomamonjy, and M. Clerc, Optimal Transport Applied to Transfer Learning For P300 Detection, BCI Conference, pp.1-6, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01556603

M. Cuturi, Sinkhorn distances: Lightspeed computation of optimal transport, NIPS, pp.2292-2300, 2013.

J. B. Stephansen, A. Ambati, E. B. Leary, H. E. Moore, O. Carrillo et al., The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy, 2017.

C. Iber, S. Ancoli-israel, A. Chesson, and S. F. Quan, The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, 2007.

S. Chambon, M. N. Galtier, P. J. Arnal, G. Wainrib, and A. Gramfort, A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.26, issue.4, 2018.
DOI : 10.1109/TNSRE.2018.2813138
URL : https://hal.archives-ouvertes.fr/hal-01810436

S. Biswal, J. Kulas, H. Sun, B. Goparaju, M. B. Westover et al., SLEEPNET: automated sleep staging system via deep learning, 2017.

A. Torralba and A. A. Efros, Unbiased look at dataset bias, CVPR 2011, pp.1521-1528, 2011.
DOI : 10.1109/CVPR.2011.5995347
URL : http://people.csail.mit.edu/torralba/publications/datasets_cvpr11.pdf

D. A. Dean, A. L. Goldberger, R. Mueller, M. Kim, M. Rueschman et al., Scaling Up Scientific Discovery in Sleep Medicine: The National Sleep Research Resource, Sleep, vol.175, issue.5, p.11511164, 2016.
DOI : 10.1093/aje/kws138
URL : https://academic.oup.com/sleep/article-pdf/39/5/1151/13679890/aasm.39.5.1151.pdf

C. O-'reilly, N. Gosselin, J. Carrier, and T. Nielsen, Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research, Journal of Sleep Research, vol.11, issue.6, pp.628-635, 2014.
DOI : 10.1038/nmeth.2855

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang et al., Automatic differentiation in PyTorch, NIPS Workshop, 2017.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, JMLR, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905