| 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 | 6105 | 2013 |
| Adversarial discriminative domain adaptation E Tzeng, J Hoffman, K Saenko, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 5635 | 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 | 3464 | 2018 |
| Deep domain confusion: Maximizing for domain invariance E Tzeng, J Hoffman, N Zhang, K Saenko, T Darrell arXiv preprint arXiv:1412.3474, 2014 | 3212 | 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 | 1607 | 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 | 893 | 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 | 859 | 2017 |
| 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 | 639 | 2017 |
| Cross Modal Distillation for Supervision Transfer S Gupta, J Hoffman, J Malik Computer Vision and Pattern Recognition (CVPR), 2016 | 634 | 2016 |
| 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 | 385 | 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 | 363 | 2013 |
| Label efficient learning of transferable representations acrosss domains and tasks Z Luo, Y Zou, J Hoffman, LF Fei-Fei Advances in neural information processing systems 30, 2017 | 339 | 2017 |
| Predictive inequity in object detection B Wilson, J Hoffman, J Morgenstern arXiv preprint arXiv:1902.11097, 2019 | 296 | 2019 |
| Token Merging: Your ViT But Faster D Bolya, CY Fu, X Dai, P Zhang, C Feichtenhofer, J Hoffman International Conference on Learning Representations (ICLR), 2023, 2022 | 274 | 2022 |
| Learning with side information through modality hallucination J Hoffman, S Gupta, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 273 | 2016 |
| Clockwork convnets for video semantic segmentation E Shelhamer, K Rakelly, J Hoffman, T Darrell Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 267 | 2016 |
| Algorithms and theory for multiple-source adaptation J Hoffman, M Mohri, N Zhang Advances in neural information processing systems 31, 2018 | 258 | 2018 |
| Discovering latent domains for multisource domain adaptation J Hoffman, B Kulis, T Darrell, K Saenko Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 226 | 2012 |
| Learning to balance specificity and invariance for in and out of domain generalization P Chattopadhyay, Y Balaji, J Hoffman ECCV, 2020 | 210 | 2020 |
| Visda: A synthetic-to-real benchmark for visual domain adaptation X Peng, B Usman, N Kaushik, D Wang, J Hoffman, K Saenko Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 207 | 2018 |
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyVerified email at eecs.berkeley.edu
