| 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 (ICML), 2013 | 4267 | 2013 |
| Adversarial discriminative domain adaptation E Tzeng, J Hoffman, K Saenko, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1974 | 2017 |
| Cycada: Cycle-consistent adversarial domain adaptation J Hoffman, E Tzeng, T Park, JY Zhu, P Isola, K Saenko, AA Efros, T Darrell ICML, 2018 | 1093 | 2018 |
| Deep domain confusion: Maximizing for domain invariance E Tzeng, J Hoffman, N Zhang, K Saenko, T Darrell arXiv preprint arXiv:1412.3474, 2014 | 1074 | 2014 |
| Simultaneous deep transfer across domains and tasks E Tzeng, J Hoffman, T Darrell, K Saenko Proceedings of the IEEE international conference on computer vision, 4068-4076, 2015 | 938 | 2015 |
| Fcns in the wild: Pixel-level adversarial and constraint-based adaptation J Hoffman, D Wang, F Yu, T Darrell arXiv preprint arXiv:1612.02649, 2016 | 339 | 2016 |
| Inferring and executing programs for visual reasoning J Johnson, B Hariharan, L Van Der Maaten, J Hoffman, L Fei-Fei, ... Proceedings of the IEEE International Conference on Computer Vision, 2989-2998, 2017 | 336 | 2017 |
| Cross Modal Distillation for Supervision Transfer S Gupta, J Hoffman, J Malik Computer Vision and Pattern Recognition (CVPR), 2016 | 313 | 2016 |
| LSDA: Large scale detection through adaptation J Hoffman, S Guadarrama, E Tzeng, R Hu, J Donahue, R Girshick, ... arXiv preprint arXiv:1407.5035, 2014 | 298 | 2014 |
| Efficient learning of domain-invariant image representations J Hoffman, E Rodner, J Donahue, T Darrell, K Saenko International Conference on Learning Representations (ICLR), 2013 | 283 | 2013 |
| Discovering latent domains for multisource domain adaptation J Hoffman, B Kulis, T Darrell, K Saenko European Conference on Computer Vision, 702-715, 2012 | 181 | 2012 |
| Label efficient learning of transferable representations across domains and tasks Z Luo, Y Zou, J Hoffman, L Fei-Fei arXiv preprint arXiv:1712.00123, 2017 | 156 | 2017 |
| Learning with side information through modality hallucination J Hoffman, S Gupta, T Darrell Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 148 | 2016 |
| 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 | 140 | 2013 |
| Clockwork convnets for video semantic segmentation E Shelhamer, K Rakelly, J Hoffman, T Darrell European Conference on Computer Vision, 852-868, 2016 | 131 | 2016 |
| Visda: The visual domain adaptation challenge X Peng, B Usman, N Kaushik, J Hoffman, D Wang, K Saenko arXiv preprint arXiv:1710.06924, 2017 | 120 | 2017 |
| Fine-grained recognition in the wild: A multi-task domain adaptation approach T Gebru, J Hoffman, L Fei-Fei Proceedings of the IEEE International Conference on Computer Vision, 1349-1358, 2017 | 107 | 2017 |
| Continuous manifold based adaptation for evolving visual domains J Hoffman, T Darrell, K Saenko Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 102 | 2014 |
| Asymmetric and category invariant feature transformations for domain adaptation J Hoffman, E Rodner, J Donahue, B Kulis, K Saenko International journal of computer vision 109 (1-2), 28-41, 2014 | 85 | 2014 |
| Best practices for fine-tuning visual classifiers to new domains B Chu, V Madhavan, O Beijbom, J Hoffman, T Darrell European conference on computer vision, 435-442, 2016 | 84 | 2016 |
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyVerified email at eecs.berkeley.edu
