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Challenges in Machine Generation of Analytic Products from Multi-Source Data Proceedings of a Workshop (2017) / Chapter Skim
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10 Session 9: Evaluation of Machine-Generated Products
Pages 34-36

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 34...
... Annotation is a critical component for deep learning in order to generate sufficient data independent of evaluation. Anthony Hoogs's company, Kitware, Inc., is the test and evaluation support contractor for the Intelligence Advanced Research Projects Activity (IARPA)
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From page 35...
... He noted that his team is designing data sets and evaluation tasks to do better factor analysis, which helps to decide the next steps in programming, evaluation, and research. Rob Fergus, New York University and Facebook, described the "sweet spot" for the amount of data that can be used for deep learning.
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From page 36...
... Duncan responded that the advantages outweigh the disadvantages for reference data sets; especially in the case of government programs, reference data sets help to focus performers on the problem of interest. As an example, Hoogs said that he could not recall a major computer vision program sponsored by the USG that had full training and testing data that was completely open and useful to the computer vision community.
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Key Terms

  • reference data
  • processing units
  • deep learning
  • hoogs added
  • fergus noted

  • video analytics
  • multiple graphical
  • task design
  • computer vision
  • processing unit

  • surveillance video
  • evaluation tasks
  • computing resources
  • little data
  • designing data

  • sufficient data
  • testing data
  • annotate data
  • video data
  • data independent

  • complete data
  • data input
  • restricted data
  • amazon offers
  • ground surveillance

  • model alignment
  • content processing
  • lajoie malik
  • industry contributions
  • community engagement


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