| Wide residual networks S Zagoruyko, N Komodakis arXiv preprint arXiv:1605.07146, 2016 | 6928 | 2016 |
| End-to-end object detection with transformers N Carion, F Massa, G Synnaeve, N Usunier, A Kirillov, S Zagoruyko Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 6310 | 2020 |
| Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer S Zagoruyko, N Komodakis arXiv preprint arXiv:1612.03928, 2016 | 1916 | 2016 |
| Learning to compare image patches via convolutional neural networks S Zagoruyko, N Komodakis Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1665 | 2015 |
| A multipath network for object detection S Zagoruyko, A Lerer, TY Lin, PO Pinheiro, S Gross, S Chintala, P Dollár arXiv preprint arXiv:1604.02135, 2016 | 270 | 2016 |
| Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer N Komodakis, S Zagoruyko ICLR, 2017 | 209 | 2017 |
| Scaling the scattering transform: Deep hybrid networks E Oyallon, E Belilovsky, S Zagoruyko Proceedings of the IEEE international conference on computer vision, 5618-5627, 2017 | 155 | 2017 |
| Wide residual networks. arXiv 2016 S Zagoruyko, N Komodakis arXiv preprint arXiv:1605.07146, 2019 | 133 | 2019 |
| Diracnets: Training very deep neural networks without skip-connections S Zagoruyko, N Komodakis arXiv preprint arXiv:1706.00388, 2017 | 96 | 2017 |
| Scattering networks for hybrid representation learning E Oyallon, S Zagoruyko, G Huang, N Komodakis, S Lacoste-Julien, ... IEEE transactions on pattern analysis and machine intelligence 41 (9), 2208-2221, 2018 | 73 | 2018 |
| Benchmarking deep learning frameworks for the classification of very high resolution satellite multispectral data M Papadomanolaki, M Vakalopoulou, S Zagoruyko, K Karantzalos ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information …, 2016 | 69 | 2016 |
| Wide residual networks. arXiv S Zagoruyko, N Komodakis arXiv preprint arXiv:1605.07146 10, 2016 | 68 | 2016 |
| Wide residual networks (2016) S Zagoruyko, N Komodakis arXiv preprint arXiv:1605.07146, 2016 | 55 | 2016 |
| 92.45% on cifar-10 in torch S Zagoruyko Torch Blog 11, 2015 | 54 | 2015 |
| Monte-carlo tree search for efficient visually guided rearrangement planning Y Labbé, S Zagoruyko, I Kalevatykh, I Laptev, J Carpentier, M Aubry, ... IEEE Robotics and Automation Letters 5 (2), 3715-3722, 2020 | 51 | 2020 |
| End-to-end object detection with transformers. arXiv 2020 N Carion, F Massa, G Synnaeve, N Usunier, A Kirillov, S Zagoruyko arXiv preprint arXiv:2005.12872, 2005 | 51 | 2005 |
| A MRF shape prior for facade parsing with occlusions M Kozinski, R Gadde, S Zagoruyko, G Obozinski, R Marlet Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 43 | 2015 |
| Polygames: Improved zero learning T Cazenave, YC Chen, GW Chen, SY Chen, XD Chiu, J Dehos, M Elsa, ... ICGA Journal 42 (4), 244-256, 2020 | 33 | 2020 |
| Deep compare: A study on using convolutional neural networks to compare image patches S Zagoruyko, N Komodakis Computer Vision and Image Understanding 164, 38-55, 2017 | 33 | 2017 |
| Compressing the input for cnns with the first-order scattering transform E Oyallon, E Belilovsky, S Zagoruyko, M Valko Proceedings of the European Conference on Computer Vision (ECCV), 301-316, 2018 | 31 | 2018 |
