Judy Hoffman
Judy Hoffman
Assistant Professor, Georgia Tech
Verified email at gatech.edu - Homepage
Title
Cited by
Cited by
Year
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
42672013
Adversarial discriminative domain adaptation
E Tzeng, J Hoffman, K Saenko, T Darrell
Proceedings of the IEEE conference on computer vision and pattern …, 2017
19742017
Cycada: Cycle-consistent adversarial domain adaptation
J Hoffman, E Tzeng, T Park, JY Zhu, P Isola, K Saenko, AA Efros, T Darrell
ICML, 2018
10932018
Deep domain confusion: Maximizing for domain invariance
E Tzeng, J Hoffman, N Zhang, K Saenko, T Darrell
arXiv preprint arXiv:1412.3474, 2014
10742014
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
9382015
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
3392016
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
3362017
Cross Modal Distillation for Supervision Transfer
S Gupta, J Hoffman, J Malik
Computer Vision and Pattern Recognition (CVPR), 2016
3132016
LSDA: Large scale detection through adaptation
J Hoffman, S Guadarrama, E Tzeng, R Hu, J Donahue, R Girshick, ...
arXiv preprint arXiv:1407.5035, 2014
2982014
Efficient learning of domain-invariant image representations
J Hoffman, E Rodner, J Donahue, T Darrell, K Saenko
International Conference on Learning Representations (ICLR), 2013
2832013
Discovering latent domains for multisource domain adaptation
J Hoffman, B Kulis, T Darrell, K Saenko
European Conference on Computer Vision, 702-715, 2012
1812012
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
1562017
Learning with side information through modality hallucination
J Hoffman, S Gupta, T Darrell
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016
1482016
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
1402013
Clockwork convnets for video semantic segmentation
E Shelhamer, K Rakelly, J Hoffman, T Darrell
European Conference on Computer Vision, 852-868, 2016
1312016
Visda: The visual domain adaptation challenge
X Peng, B Usman, N Kaushik, J Hoffman, D Wang, K Saenko
arXiv preprint arXiv:1710.06924, 2017
1202017
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
1072017
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
1022014
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
852014
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
842016
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