| 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 | 543 | 2013 |
| 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 | 262 | 2019 |
| Learning to diversify for single domain generalization Z Wang, Y Luo, R Qiu, Z Huang, M Baktashmotlagh Proceedings of the IEEE/CVF International Conference on Computer Vision, 834-843, 2021 | 218 | 2021 |
| 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 | 149 | 2014 |
| Correlation-aware adversarial domain adaptation and generalization MM Rahman, C Fookes, M Baktashmotlagh, S Sridharan Pattern Recognition 100, 107124, 2020 | 136 | 2020 |
| 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 | 127 | 2019 |
| Distribution-matching embedding for visual domain adaptation M Baktashmotlagh, M Har, M Salzmann Journal of Machine Learning Research 17 (108), 1-30, 2016 | 120 | 2016 |
| Progressive Graph Learning for Open-Set Domain Adaptation Y Luo, Z Wang, Z Huang, M Baktashmotlagh 37th International Conference on Machine Learning, 2020 | 106 | 2020 |
| 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 | 76 | 2019 |
| Visualizing student opinion through text analysis S Cunningham-Nelson, M Baktashmotlagh, W Boles IEEE Transactions on Education 62 (4), 305-311, 2019 | 74 | 2019 |
| On minimum discrepancy estimation for deep domain adaptation MM Rahman, C Fookes, M Baktashmotlagh, S Sridharan Domain Adaptation for Visual Understanding, 81-94, 2020 | 66 | 2020 |
| Learning factorized representations for open-set domain adaptation M Baktashmotlagh, M Faraki, T Drummond, M Salzmann ICLR, 2019 | 66 | 2019 |
| 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 | 64 | 2015 |
| 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 | 55 | 2016 |
| Adversarial bipartite graph learning for video domain adaptation Y Luo, Z Huang, Z Wang, Z Zhang, M Baktashmotlagh Proceedings of the 28th ACM International Conference on Multimedia, 19-27, 2020 | 47 | 2020 |
| 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 | 46 | 2015 |
| Robust re-identification of manta rays from natural markings by learning pose invariant embeddings O Moskvyak, F Maire, F Dayoub, AO Armstrong, M Baktashmotlagh 2021 Digital Image Computing: Techniques and Applications (DICTA), 1-8, 2021 | 44 | 2021 |
| 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 | 44 | 2014 |
| Closing the gap of simulation to reality in electromagnetic imaging of brain strokes via deep neural networks A Al-Saffar, A Bialkowski, M Baktashmotlagh, A Trakic, L Guo, A Abbosh IEEE Transactions on Computational Imaging 7, 13-21, 2020 | 37 | 2020 |
| Keypoint-Aligned Embeddings for Image Retrieval and Re-identification O Moskvyak, F Maire, F Dayoub, M Baktashmotlagh Winter Conference on Applications of Computer Vision, 2020 | 32 | 2020 |
Mathieu SalzmannEPFLПотвърден имейл адрес: epfl.ch
