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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 257 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 | 251 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 74 | 29% |
| Student > Master | 55 | 21% |
| Researcher | 46 | 18% |
| Student > Bachelor | 24 | 9% |
| Other | 15 | 6% |
| Other | 19 | 7% |
| Unknown | 24 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 177 | 69% |
| Engineering | 26 | 10% |
| Mathematics | 6 | 2% |
| Agricultural and Biological Sciences | 4 | 2% |
| Physics and Astronomy | 4 | 2% |
| Other | 10 | 4% |
| Unknown | 30 | 12% |