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Chapter title |
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
|
---|---|
Chapter number | 19 |
Book title |
Computer Vision – ECCV 2016
|
Published by |
Springer, Cham, October 2016
|
DOI | 10.1007/978-3-319-46487-9_19 |
Book ISBNs |
978-3-31-946486-2, 978-3-31-946487-9
|
Authors |
Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, Li Fei-Fei |
Mendeley readers
The data shown below were compiled from readership statistics for 251 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 2 | <1% |
Italy | 1 | <1% |
China | 1 | <1% |
United States | 1 | <1% |
Luxembourg | 1 | <1% |
Unknown | 245 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 71 | 28% |
Student > Master | 53 | 21% |
Researcher | 45 | 18% |
Student > Bachelor | 23 | 9% |
Other | 15 | 6% |
Other | 21 | 8% |
Unknown | 23 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 173 | 69% |
Engineering | 27 | 11% |
Mathematics | 6 | 2% |
Physics and Astronomy | 4 | 2% |
Environmental Science | 4 | 2% |
Other | 8 | 3% |
Unknown | 29 | 12% |