Jeff Donahue
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As of May 2017, I'm working as a Research Scientist at DeepMind in London.

I graduated from UC Berkeley in May 2017 with a Ph.D. in Computer Science, advised by Trevor Darrell. My dissertation and research focused on artificial intelligence, computer vision, and machine learning.

I graduated from UT Austin in May 2011 with a B.S. in Computer Science, completing a Turing Scholars degree with an honors thesis (December 2010) supervised by Kristen Grauman.


Publications (see Google Scholar for most up-to-date list)

J. Donahue, P. Krähenbühl, T. Darrell.
Adversarial Feature Learning [pdf, code].
In Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France, April 2017.

L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, T. Darrell.
Generating Visual Explanations [pdf].
In Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, October 2016.

D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, A. Efros.
Context Encoders: Feature Learning by Inpainting [pdf].
In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, June 2016.

P. Krähenbühl, C. Doersch, J. Donahue, T. Darrell.
Data-dependent Initializations of Convolutional Neural Networks [pdf].
In Proceedings of the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, May 2016.

S. Venugopalan, M. Rohrbach, J. Donahue, R. Mooney, T. Darrell, K. Saenko.
Sequence to Sequence -- Video to Text [pdf].
In Proceedings of the International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015.

J. Donahue, L. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, T. Darrell.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description [pdf].
In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR, oral), Boston, Massachusetts, June 2015.

J. Hoffman, S. Guadarrama, E. Tzeng, R. Hu, J. Donahue, R. Girshick, T. Darrell, K. Saenko.
LSDA: Large Scale Detection Through Adaptation [pdf].
To appear in Proceedings of the Neural Information Processing Systems Conference (NIPS), Montreal, Canada, December 2014.

S. Guadarrama, E. Rodner, K. Saenko, N. Zhang, R. Farrell, J. Donahue, T. Darrell.
Open-vocabulary Object Retrieval.
In Proceedings of the Robotics: Science and Systems Conference (RSS, oral), Berkeley, California, July 2014.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell.
Caffe: Convolutional Architecture for Fast Feature Embedding [pdf (arXiv)].
arXiv:1408.5093, June 2014.
Winner of the 2014 ACM MM Open Source Software Competition! (Orlando, Florida, November 2014)
Download Caffe

N. Zhang, J. Donahue, R. Girshick, T. Darrell.
Part-based R-CNNs for Fine-grained Category Detection [pdf (arXiv)].
In Proceedings of the European Conference on Computer Vision (ECCV, oral), Zurich, Switzerland, September 2014.

R. Girshick, J. Donahue, T. Darrell, J. Malik.
Rich feature hierarchies for accurate object detection and semantic segmentation [extended pdf (arXiv)].
In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR, oral), Columbus, Ohio, June 2014.

J. Donahue*, Y. Jia*, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, T. Darrell.
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition [pdf].
In Proceedings of the International Conference on Machine Learning (ICML), Beijing, China, June 2014.

E. Rodner, J. Hoffman, J. Donahue, T. Darrell, and K. Saenko.
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations [pdf].
arXiv:1308.4200, August 2013.

J. Donahue, J. Hoffman, E. Rodner, K. Saenko, and T. Darrell.
Semi-Supervised Domain Adaptation with Instance Constraints.
In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, June 2013.

J. Hoffman, E. Rodner, J. Donahue, K. Saenko, and T. Darrell.
Efficient Learning of Domain-Invariant Image Representations.
In Proceedings of the International Conference on Learning Representations (ICLR), Scottsdale, Arizona, May 2013.

J. Donahue and K. Grauman.
Annotator Rationales for Visual Recognition.
In Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011.

Industry Experience

Fall 2014 to January 2017: part-time engineer with Pinterest - Visual Discovery

Summer 2014: interned with Pinterest - Visual Discovery

Summer 2010-2012: interned with Google - Image Search Annotation (Summer 2012), Mobile Search Ad Quality (Summer 2011), YouTube Content Management System (Summer 2010)

Summer 2008-2009: interned with National Instruments - LabVIEW EIO Variable Architecture (Summer 2009), LabVIEW FPGA (Summer 2008)


Other Stuff

Wrote Smozzy, an Android app that lets users without a data plan browse the web through text messaging (covered in several tech journals, including TechCrunch, CNET, Engadget, etc.).


Last updated 2017/05/19.