| Domain-adversarial training of neural networks Y Ganin, E Ustinova, H Ajakan, P Germain, H Larochelle, F Laviolette, ... The Journal of Machine Learning Research 17 (1), 2096-2030, 2016 | 2667 | 2016 |
| Unsupervised domain adaptation by backpropagation Y Ganin, V Lempitsky International Conference on Machine Learning (ICML 2015), 2015 | 2083 | 2015 |
| Instance normalization: The missing ingredient for fast stylization D Ulyanov, A Vedaldi, V Lempitsky arXiv preprint arXiv:1607.08022, 2016 | 1271 | 2016 |
| Neural codes for image retrieval A Babenko, A Slesarev, A Chigorin, V Lempitsky European conference on computer vision, 584-599, 2014 | 1034 | 2014 |
| The devil is in the details: an evaluation of recent feature encoding methods. K Chatfield, VS Lempitsky, A Vedaldi, A Zisserman BMVC 2 (4), 8, 2011 | 1018 | 2011 |
| Learning to count objects in images V Lempitsky, A Zisserman Advances in neural information processing systems 23, 1324-1332, 2010 | 814 | 2010 |
| Deep image prior D Ulyanov, A Vedaldi, V Lempitsky Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 804 | 2018 |
| Class-specific hough forests for object detection J Gall, V Lempitsky IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1022 - 1029, 2009 | 769* | 2009 |
| Aggregating local deep features for image retrieval A Babenko, V Lempitsky Proceedings of the IEEE international conference on computer vision, 1269-1277, 2015 | 684* | 2015 |
| Hough forests for object detection, tracking, and action recognition J Gall, A Yao, N Razavi, L Van Gool, V Lempitsky IEEE transactions on pattern analysis and machine intelligence 33 (11), 2188 …, 2011 | 659 | 2011 |
| Texture Networks: Feed-forward Synthesis of Textures and Stylized Images D Ulyanov, V Lebedev, A Vedaldi, V Lempitsky International Conference on Machine Learning (ICML 2016), 2016 | 554 | 2016 |
| Optimizing binary MRFs via extended roof duality C Rother, V Kolmogorov, V Lempitsky, M Szummer 2007 IEEE conference on computer vision and pattern recognition, 1-8, 2007 | 522 | 2007 |
| Speeding-up convolutional neural networks using fine-tuned cp-decomposition V Lebedev, Y Ganin, M Rakhuba, I Oseledets, V Lempitsky arXiv preprint arXiv:1412.6553, 2014 | 520 | 2014 |
| Escape from cells: Deep kd-networks for the recognition of 3d point cloud models R Klokov, V Lempitsky Proceedings of the IEEE International Conference on Computer Vision, 863-872, 2017 | 487 | 2017 |
| Image segmentation with a bounding box prior V Lempitsky, P Kohli, C Rother, T Sharp 2009 IEEE 12th international conference on computer vision, 277-284, 2009 | 386 | 2009 |
| On detection of multiple object instances using hough transforms O Barinova, V Lempitsky, P Kholi IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (9), 1773-1784, 2012 | 368 | 2012 |
| Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis D Ulyanov, A Vedaldi, V Lempitsky Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 318 | 2017 |
| The inverted multi-index A Babenko, V Lempitsky IEEE transactions on pattern analysis and machine intelligence 37 (6), 1247-1260, 2014 | 315* | 2014 |
| Markov random fields for vision and image processing A Blake, P Kohli, C Rother Mit Press, 2011 | 310 | 2011 |
| Fast convnets using group-wise brain damage V Lebedev, V Lempitsky Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 292 | 2016 |
