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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
  • Among the highest-scoring outputs from this source (#17 of 444,059)
  • 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
9 news outlets
blogs
4 blogs
twitter
1187 tweeters
weibo
5 weibo users
facebook
33 Facebook pages
googleplus
66 Google+ users
reddit
5 Redditors
q&a
2 Q&A threads

Readers on

mendeley
1189 Mendeley
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

Twitter Demographics

The data shown below were collected from the profiles of 1,187 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1,189 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 28 2%
Germany 17 1%
United Kingdom 15 1%
China 12 1%
Italy 9 <1%
Spain 6 <1%
Japan 6 <1%
Singapore 3 <1%
Turkey 2 <1%
Other 20 2%
Unknown 1071 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 392 33%
Student > Master 258 22%
Researcher 186 16%
Student > Bachelor 133 11%
Other 61 5%
Other 159 13%
Readers by discipline Count As %
Computer Science 779 66%
Engineering 182 15%
Agricultural and Biological Sciences 55 5%
Physics and Astronomy 49 4%
Mathematics 26 2%
Other 98 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1060. 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 16 June 2017.
All research outputs
#1,424
of 7,937,033 outputs
Outputs from arXiv
#17
of 444,059 outputs
Outputs of similar age
#56
of 238,001 outputs
Outputs of similar age from arXiv
#2
of 22,169 outputs
Altmetric has tracked 7,937,033 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 444,059 research outputs from this source. They receive a mean Attention Score of 2.7. 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 238,001 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 22,169 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.