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UseML (IKT of the Future)


Improving the usability of machine learning in industrial inspection systems.

The overall goal of the project is to enable the end-user to adequately deal with the complexity of automatic inspection systems during the set-up phase and during maintenance.  The project aims at the following goals:

  • Active learning and in-line training:
    • developing methodologies which reduce the requested number of class labels during off-line training and on-line adaptation of classifiers
    • synthesizing artificial defect images that can be presented to acquire input in areas, where no real samples could yet be obtained
    • detecting a systematic change in the decision making (concept drift) and to efficiently update or correct earlier decision without the need of re-labelling a large part of the previous samples.
  • Adding new defect classes:
    • acquiring high-level information about the defect class from the end-user in the presence of very little concrete data
    • data base search for similar samples and refining decision boundaries
  • Explaining and correcting false decisions:
    • making progress in interpretability and representation of classifiers
    • enabling the user to modify structural components and decision boundaries
    • addressing reliability concepts (interpretability of classifiers outputs, reason finding)


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