Jeff Donahue
Jeff Donahue
Research Scientist, DeepMind
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
Large scale GAN training for high fidelity natural image synthesis
A Brock, J Donahue, K Simonyan
arXiv preprint arXiv:1809.11096, 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
Adversarial feature learning
J Donahue, P Krähenbühl, T Darrell
arXiv preprint arXiv:1605.09782, 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
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
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
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
Population based training of neural networks
M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ...
arXiv preprint arXiv:1711.09846, 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
Large scale adversarial representation learning
J Donahue, K Simonyan
Advances in neural information processing systems 32, 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
Efficient learning of domain-invariant image representations
J Hoffman, E Rodner, J Donahue, T Darrell, K Saenko
arXiv preprint arXiv:1301.3224, 2013
Data-dependent initializations of convolutional neural networks
P Krähenbühl, C Doersch, J Donahue, T Darrell
arXiv preprint arXiv:1511.06856, 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
End-to-end adversarial text-to-speech
J Donahue, S Dieleman, M Bińkowski, E Elsen, K Simonyan
arXiv preprint arXiv:2006.03575, 2020
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