| Unsupervised domain adaptation by domain invariant projection M Baktashmotlagh, MT Harandi, BC Lovell, M Salzmann Proceedings of the IEEE International Conference on Computer Vision, 769-776, 2013 | 349 | 2013 |
| Domain adaptation on the statistical manifold M Baktashmotlagh, MT Harandi, BC Lovell, M Salzmann Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 92 | 2014 |
| Domain adaptation in computer vision applications G Csurka Springer International Publishing, 2017 | 65 | 2017 |
| Distribution-matching embedding for visual domain adaptation M Baktashmotlagh, M Harandi, M Salzmann Journal of Machine Learning Research 17, Article number: 108 1-30, 2016 | 59 | 2016 |
| Beyond gauss: Image-set matching on the riemannian manifold of pdfs M Harandi, M Salzmann, M Baktashmotlagh Proceedings of the IEEE International Conference on Computer Vision, 4112-4120, 2015 | 52 | 2015 |
| Deep level sets: Implicit surface representations for 3d shape inference M Michalkiewicz, JK Pontes, D Jack, M Baktashmotlagh, A Eriksson arXiv preprint arXiv:1901.06802, 2019 | 49 | 2019 |
| Discriminative non-linear stationary subspace analysis for video classification M Baktashmotlagh, M Harandi, BC Lovell, M Salzmann IEEE transactions on pattern analysis and machine intelligence 36 (12), 2353 …, 2014 | 30 | 2014 |
| Learning factorized representations for open-set domain adaptation M Baktashmotlagh, M Faraki, T Drummond, M Salzmann ICLR, 2019 | 28 | 2019 |
| Implicit surface representations as layers in neural networks M Michalkiewicz, JK Pontes, D Jack, M Baktashmotlagh, A Eriksson Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 28 | 2019 |
| R1SVM: a Randomised Nonlinear Approach to Large-Scale Anomaly Detection S Erfani, M Baktashmotlagh, S Rajasegarar, S Karunasekera, C Leckie Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015 | 28 | 2015 |
| Directional space-time oriented gradients for 3d visual pattern analysis E Norouznezhad, MT Harandi, A Bigdeli, M Baktash, A Postula, BC Lovell European Conference on Computer Vision, 736-749, 2012 | 21 | 2012 |
| Robust domain generalisation by enforcing distribution invariance S Erfani, M Baktashmotlagh, M Moshtaghi, X Nguyen, C Leckie, J Bailey, ... Proceedings of the Twenty-Fifth International Joint Conference on Artificial …, 2016 | 16 | 2016 |
| On minimum discrepancy estimation for deep domain adaptation MM Rahman, C Fookes, M Baktashmotlagh, S Sridharan Domain Adaptation for Visual Understanding, 81-94, 2020 | 12 | 2020 |
| R1stm: One-class support tensor machine with randomised kernel SM Erfani, M Baktashmotlagh, S Rajasegarad, V Nguyen, C Leckie, ... Proceedings of the 2016 SIAM International Conference on Data Mining, 198-206, 2016 | 12 | 2016 |
| Correlation-aware adversarial domain adaptation and generalization MM Rahman, C Fookes, M Baktashmotlagh, S Sridharan Pattern Recognition 100, 107124, 2020 | 10 | 2020 |
| Dynamic resource aware sensor networks: Integration of sensor cloud and ERPs M Baktashmotlagh, A Bigdeli, BC Lovell 2011 8th IEEE International Conference on Advanced Video and Signal Based …, 2011 | 9 | 2011 |
| Multi-component image translation for deep domain generalization MM Rahman, C Fookes, M Baktashmotlagh, S Sridharan 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 579-588, 2019 | 8 | 2019 |
| Non-linear stationary subspace analysis with application to video classification M Baktashmotlagh, M Harandi, A Bigdeli, B Lovell, M Salzmann International Conference on Machine Learning, 450-458, 2013 | 8 | 2013 |
| From shared subspaces to shared landmarks: A robust multi-source classification approach S Erfani, M Baktashmotlagh, M Moshtaghi, V Nguyen, C Leckie, J Bailey, ... Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 7 | 2017 |
| Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling Y Luo, Z Huang, Z Zhang, Z Wang, M Baktashmotlagh, Y Yang AAAI Conference on Artificial Intelligence, 2020 | 6 | 2020 |
Mathieu SalzmannEPFLVerified email at epfl.ch
