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Expected Model Output Change (EMOC)

Source code for methods described in the following papers:

  • Active learning and discovery of object categories in the presence of unnameable instances, C Käding, A Freytag, E Rodner, P Bodesheim, J Denzler, Computer Vision and Pattern Recognition (CVPR), 2015

  • Large-Scale Active Learning with Approximations of Expected Model Output Changes, C Käding, A Freytag, E Rodner, A Perino, J Denzler, German Conference on Pattern Recognition (GCPR), 2016

  • Watch, Ask, Learn, and Improve: A Lifelong Learning Cycle for Visual Recognition, C Käding, E Rodner, A Freytag, J Denzler, European Symposium on Artificial Neural Networks (ESANN), 2016

If you use parts of the code, please cite the corresponding papers.

  • Python 2.7
  • numpy
  • scipy
  • scikit-learn
  1. define setup (see example_setup.cfg)
  2. precompute setup (run evaluation/ setup.cfg)
  3. start experiment (run evaluation/ setup.cfg)
  4. see results (stored in results.mat)