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 (#15 of 416,134)
  • 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
1142 tweeters
weibo
5 weibo users
facebook
32 Facebook pages
googleplus
66 Google+ users
reddit
5 Redditors
q&a
2 Q&A threads

Readers on

mendeley
269 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 1,142 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 269 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
United Kingdom 3 1%
Germany 2 <1%
France 2 <1%
Australia 1 <1%
Brazil 1 <1%
Luxembourg 1 <1%
Turkey 1 <1%
Sweden 1 <1%
Other 5 2%
Unknown 245 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 76 28%
Student > Master 72 27%
Researcher 39 14%
Student > Bachelor 23 9%
Other 15 6%
Other 44 16%
Readers by discipline Count As %
Computer Science 183 68%
Engineering 29 11%
Physics and Astronomy 14 5%
Psychology 11 4%
Agricultural and Biological Sciences 7 3%
Other 25 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1036. 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 25 March 2017.
All research outputs
#1,289
of 7,437,114 outputs
Outputs from arXiv
#15
of 416,134 outputs
Outputs of similar age
#62
of 234,519 outputs
Outputs of similar age from arXiv
#2
of 22,182 outputs
Altmetric has tracked 7,437,114 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 416,134 research outputs from this source. They receive a mean Attention Score of 2.6. 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 234,519 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 22,182 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.