Learning Concept Embeddings with Combined Human-Machine Expertise

@article{Wilber2015LearningCE,
  title={Learning Concept Embeddings with Combined Human-Machine Expertise},
  author={Michael J. Wilber and I. Kwak and D. Kriegman and Serge J. Belongie},
  journal={2015 IEEE International Conference on Computer Vision (ICCV)},
  year={2015},
  pages={981-989}
}
  • Michael J. Wilber, I. Kwak, +1 author Serge J. Belongie
  • Published 2015
  • Computer Science
  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • This paper presents our work on "SNaCK," a low-dimensional concept embedding algorithm that combines human expertise with automatic machine similarity kernels. Both parts are complimentary: human insight can capture relationships that are not apparent from the object's visual similarity and the machine can help relieve the human from having to exhaustively specify many constraints. We show that our SNaCK embeddings are useful in several tasks: distinguishing prime and nonprime numbers on MNIST… CONTINUE READING
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