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Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

Overview of attention for article published in arXiv, December 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
16 news outlets
blogs
10 blogs
twitter
1014 X users
patent
1 patent
weibo
5 weibo users
facebook
33 Facebook pages
wikipedia
3 Wikipedia pages
googleplus
67 Google+ users
reddit
6 Redditors
q&a
2 Q&A threads

Readers on

mendeley
3199 Mendeley
citeulike
2 CiteULike
Title
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Published in
arXiv, December 2014
Authors

Anh Nguyen, Jason Yosinski, Jeff Clune

X Demographics

X Demographics

The data shown below were collected from the profiles of 1,014 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3,199 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 34 1%
Germany 17 <1%
United Kingdom 17 <1%
China 9 <1%
Italy 8 <1%
Japan 7 <1%
Australia 4 <1%
Spain 4 <1%
Turkey 3 <1%
Other 23 <1%
Unknown 3073 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 814 25%
Student > Master 625 20%
Researcher 421 13%
Student > Bachelor 330 10%
Other 142 4%
Other 390 12%
Unknown 477 15%
Readers by discipline Count As %
Computer Science 1614 50%
Engineering 429 13%
Physics and Astronomy 96 3%
Agricultural and Biological Sciences 77 2%
Neuroscience 68 2%
Other 337 11%
Unknown 578 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1027. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 September 2023.
All research outputs
#15,771
of 25,782,917 outputs
Outputs from arXiv
#140
of 944,601 outputs
Outputs of similar age
#96
of 369,352 outputs
Outputs of similar age from arXiv
#2
of 11,133 outputs
Altmetric has tracked 25,782,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 944,601 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 369,352 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 11,133 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.