| U-net: Convolutional networks for biomedical image segmentation O Ronneberger, P Fischer, T Brox Medical image computing and computer-assisted intervention–MICCAI 2015: 18th …, 2015 | 102708 | 2015 |
| 3D U-Net: learning dense volumetric segmentation from sparse annotation Ö Çiçek, A Abdulkadir, SS Lienkamp, T Brox, O Ronneberger Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th …, 2016 | 8520 | 2016 |
| Striving for simplicity: The all convolutional net JT Springenberg, A Dosovitskiy, T Brox, M Riedmiller arXiv preprint arXiv:1412.6806, 2014 | 6197 | 2014 |
| Flownet: Learning optical flow with convolutional networks A Dosovitskiy, P Fischer, E Ilg, C Hazirbas, V Golkov, P van der Smagt, ... 2015 IEEE International Conference on Computer Vision (ICCV), 2758-2766, 2015 | 5463* | 2015 |
| Flownet 2.0: Evolution of optical flow estimation with deep networks E Ilg, N Mayer, T Saikia, M Keuper, A Dosovitskiy, T Brox Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 3957 | 2017 |
| High accuracy optical flow estimation based on a theory for warping T Brox, A Bruhn, N Papenberg, J Weickert Computer Vision-ECCV 2004: 8th European Conference on Computer Vision …, 2004 | 3844 | 2004 |
| A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation N Mayer, E Ilg, P Hausser, P Fischer, D Cremers, A Dosovitskiy, T Brox Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 3318 | 2016 |
| Discriminative unsupervised feature learning with convolutional neural networks A Dosovitskiy, JT Springenberg, M Riedmiller, T Brox Advances in neural information processing systems 27, 2014 | 2110 | 2014 |
| U-Net: deep learning for cell counting, detection, and morphometry T Falk, D Mai, R Bensch, Ö Çiçek, A Abdulkadir, Y Marrakchi, A Böhm, ... Nature methods 16 (1), 67-70, 2019 | 1935 | 2019 |
| Large displacement optical flow: descriptor matching in variational motion estimation T Brox, J Malik IEEE transactions on pattern analysis and machine intelligence 33 (3), 500-513, 2010 | 1829 | 2010 |
| Generating images with perceptual similarity metrics based on deep networks A Dosovitskiy, T Brox Advances in neural information processing systems 29, 2016 | 1386 | 2016 |
| Learning to generate chairs with convolutional neural networks A Dosovitskiy, J Tobias Springenberg, T Brox Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1060 | 2015 |
| Object segmentation by long term analysis of point trajectories T Brox, J Malik European conference on computer vision, 282-295, 2010 | 1045 | 2010 |
| Towards total recall in industrial anomaly detection K Roth, L Pemula, J Zepeda, B Schölkopf, T Brox, P Gehler Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 1035 | 2022 |
| Sparsity invariant cnns J Uhrig, N Schneider, L Schneider, U Franke, T Brox, A Geiger 2017 international conference on 3D Vision (3DV), 11-20, 2017 | 1034 | 2017 |
| Inverting visual representations with convolutional networks A Dosovitskiy, T Brox Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 930* | 2016 |
| Learning to estimate 3d hand pose from single rgb images C Zimmermann, T Brox Proceedings of the IEEE international conference on computer vision, 4903-4911, 2017 | 915 | 2017 |
| Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs M Tatarchenko, A Dosovitskiy, T Brox Proceedings of the IEEE international conference on computer vision, 2088-2096, 2017 | 903 | 2017 |
| Synthesizing the preferred inputs for neurons in neural networks via deep generator networks A Nguyen, A Dosovitskiy, J Yosinski, T Brox, J Clune Advances in neural information processing systems 29, 2016 | 881 | 2016 |
| Demon: Depth and motion network for learning monocular stereo B Ummenhofer, H Zhou, J Uhrig, N Mayer, E Ilg, A Dosovitskiy, T Brox Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 868 | 2017 |
Olaf Ronnebergerapl. Prof. for Computer Science, University of Freiburg, Germany確認したメール アドレス: cs.uni-freiburg.de