| Rich feature hierarchies for accurate object detection and semantic segmentation R Girshick, J Donahue, T Darrell, J Malik Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 33944 | 2014 |
| Caffe: Convolutional architecture for fast feature embedding Y Jia, E Shelhamer, J Donahue, S Karayev, J Long, R Girshick, ... Proceedings of the 22nd ACM international conference on Multimedia, 675-678, 2014 | 17929 | 2014 |
| Long-term recurrent convolutional networks for visual recognition and description J Donahue, L Anne Hendricks, S Guadarrama, M Rohrbach, ... Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 7309 | 2015 |
| Decaf: A deep convolutional activation feature for generic visual recognition J Donahue, Y Jia, O Vinyals, J Hoffman, N Zhang, E Tzeng, T Darrell International conference on machine learning, 647-655, 2014 | 5798 | 2014 |
| Context encoders: Feature learning by inpainting D Pathak, P Krahenbuhl, J Donahue, T Darrell, AA Efros Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 5787 | 2016 |
| Large scale GAN training for high fidelity natural image synthesis A Brock, J Donahue, K Simonyan arXiv preprint arXiv:1809.11096, 2018 | 4878 | 2018 |
| Region-based convolutional networks for accurate object detection and segmentation R Girshick, J Donahue, T Darrell, J Malik IEEE transactions on pattern analysis and machine intelligence 38 (1), 142-158, 2015 | 2927 | 2015 |
| Adversarial feature learning J Donahue, P Krähenbühl, T Darrell arXiv preprint arXiv:1605.09782, 2016 | 2209 | 2016 |
| Sequence to sequence-video to text S Venugopalan, M Rohrbach, J Donahue, R Mooney, T Darrell, K Saenko Proceedings of the IEEE international conference on computer vision, 4534-4542, 2015 | 1646 | 2015 |
| Part-based R-CNNs for fine-grained category detection N Zhang, J Donahue, R Girshick, T Darrell Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014 | 1386 | 2014 |
| Translating videos to natural language using deep recurrent neural networks S Venugopalan, H Xu, J Donahue, M Rohrbach, R Mooney, K Saenko arXiv preprint arXiv:1412.4729, 2014 | 1153 | 2014 |
| Flamingo: a visual language model for few-shot learning JB Alayrac, J Donahue, P Luc, A Miech, I Barr, Y Hasson, K Lenc, ... Advances in Neural Information Processing Systems 35, 23716-23736, 2022 | 1095 | 2022 |
| Population based training of neural networks M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ... arXiv preprint arXiv:1711.09846, 2017 | 777 | 2017 |
| Generating visual explanations LA Hendricks, Z Akata, M Rohrbach, J Donahue, B Schiele, T Darrell Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 678 | 2016 |
| Large scale adversarial representation learning J Donahue, K Simonyan Advances in neural information processing systems 32, 2019 | 525 | 2019 |
| LSDA: Large scale detection through adaptation J Hoffman, S Guadarrama, ES Tzeng, R Hu, J Donahue, R Girshick, ... Advances in neural information processing systems 27, 2014 | 364 | 2014 |
| Efficient learning of domain-invariant image representations J Hoffman, E Rodner, J Donahue, T Darrell, K Saenko arXiv preprint arXiv:1301.3224, 2013 | 335 | 2013 |
| Data-dependent initializations of convolutional neural networks P Krähenbühl, C Doersch, J Donahue, T Darrell arXiv preprint arXiv:1511.06856, 2015 | 233 | 2015 |
| Semi-supervised domain adaptation with instance constraints J Donahue, J Hoffman, E Rodner, K Saenko, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2013 | 192 | 2013 |
| End-to-end adversarial text-to-speech J Donahue, S Dieleman, M Bińkowski, E Elsen, K Simonyan arXiv preprint arXiv:2006.03575, 2020 | 185 | 2020 |
